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Academic journals are published much more frequently than books.

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Fundamentals of graphic design—essential tools for effective visual science communication

Information & authors, metrics & citations, view options, science communication and the visual design gap, support for visual communication, a primer in graphic design, typefaces and fonts, when to do-it-yourself or consult a graphic designer, how to find a graphic designer and what to expect in the creative process, conclusions, acknowledgements, information, published in.

Data Availability Statement

• science communication
• graphic design
• visual communication
• visual design
• information design
• public engagement
• Integrative Sciences
• Science Communication

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Science–graphic art partnerships to increase research impact

Colin k. khoury.

1 International Center for Tropical Agriculture (CIAT), Km 17, Recta Cali-Palmira, Apartado Aéreo 6713, 763537 Cali, Colombia

2 Independent Artist, San Jose, CA USA

Michael Kantar

3 Department of Tropical Plant and Soil Science, University of Hawaii at Manoa, 3190 Malie Way, Honolulu, HI 96822 USA

Ellie Barber

4 Aspen Global Change Institute, 104 Midland Ave #205, Basalt, CO 81621 USA

Vincent Ricciardi

5 The Institute for Resources, Environment, and Sustainability, University of British Columbia, 429-2202 Main Mall, Vancouver, BC V6T 1Z4 Canada

6 School of Public Policy and Global Affairs, University of British Columbia, 251-1855 West Mall, Vancouver, BC V6T 1Z2 Canada

Carni Klirs

7 World Resources Institute, 10G Street, NE Suite 800, Washington, D.C. 20002 USA

Leah Kucera

8 SERVIR Science Coordination Office, NASA Marshall Space Flight Center, National Space Science & Technology Center, 320 Sparkman Drive, Huntsville, AL 35805 USA

Zia Mehrabi

Nathanael johnson.

9 Grist, 1201 Western Ave., Suite 410, Seattle, WA 98101 USA

Simone Klabin

10 Independent Author, New York, NY USA

Álvaro Valiño

11 Independent Information Designer, A Coruña, Spain

Kelsey Nowakowski

12 Independent Information Designer, San Juan, Puerto Rico USA

Ignasi Bartomeus

13 Integrative Ecology Department, Estación Biológica de Doñana (EBD-CSIC), Avenida Américo Vespucio 26, Isla de la Cartuja, Sevilla, E-41092 Spain

Navin Ramankutty

Allison miller.

14 St. Louis University, Department of Biology, 3507 Laclede Avenue, St. Louis, MO 63103 USA

15 Danforth Plant Science Center, 975 North Warson Road, St. Louis, MO 63132 USA

Meagan Schipanski

16 Department of Soil and Crop Sciences, Colorado State University, Fort Collins, CO 80523-1170 USA

Michael A. Gore

17 Plant Breeding and Genetics Section, School of Integrative Plant Science, Cornell University, Ithaca, NY 14853 USA

18 San Diego Botanic Garden, 230 Quail Gardens Drive, Encinitas, CA 92024 USA

Associated Data

All infographics produced in this project available from the Dryad Digital Repository: 10.5061/dryad.7j5d5t0 6 .

Graphics are becoming increasingly important for scientists to effectively communicate their findings to broad audiences, but most researchers lack expertise in visual media. We suggest collaboration between scientists and graphic designers as a way forward and discuss the results of a pilot project to test this type of collaboration.

When we think of groundbreaking scientific advances, it is often in visual terms – the first depictions of the structure of DNA; Darwin’s sketches of the tree of life; even DaVinci’s Vetruvian Man . The power of these pictures to speak to people, especially those outside our specialized research communities, is worth far more than a thousand words.

Scientists’ need for visual art has never been greater. More sophisticated graphics are required to communicate the results of ever more complex and transdisciplinary research. Well-constructed graphics can widen the impact of research articles striving to be noticed in an ever-increasing flood of published work, and supplementary visuals, for instance graphical abstracts, are often now requested by journals, if not required 1 . Funders are also increasingly emphasizing the value of graphics in grant proposals 2 . Online, where viewers decide whether to engage with material within a matter of seconds 3 , compelling visuals are pivotal, especially as research organizations incorporate social media attention in their impact metrics.

While many researchers are rising to the challenge of communicating their work via social media and other formats beyond their traditional channels 4 , very few scientists have expertise in visual media communications, and even fewer in design tailored for online platforms. Learning the specialized skills needed to create graphics for the changing array of conventional and new science media is a very big ask.

But scientists do not need to go it alone. Collaborations between researchers, graphic designers, and other visual communications professionals offer great potential (Box 1 ).

Test project overview

Recently, we tested the efficacy of scientist–graphic artist collaborations by pairing six research laboratories involved in different aspects of biological and agricultural sciences with graphic designers and media content creators. The work of the eight participating scientists focused on complex, societally relevant subjects within biology, food, and agriculture, including pollinators and threats to biodiversity, modern plant breeding, agricultural development and land use change, phenomics and other new agricultural technologies, agricultural sustainability, and the origins and domestication processes of food plants.

The five participating artists were chosen for their track records as producers of attractive and interesting visual online media, either as graphic design professionals or as talented hobbyists. Some had research backgrounds while others had no science training. All of the scientists and graphic designers approached were enthusiastic about experimenting with this cross-disciplinary collaboration. The researchers and designers were paired based on the artists’ interests among the scientific topics, and the designers were compensated for their contributions. The scientist–artist pairs were asked to create infographics – in this case defined as visually arresting, quickly understandable, graphical representations of scientific research – based on the research laboratories’ current projects, within three months.

At the end of this time, the researchers and artists, supplemented by additional professionals and experts in graphic design and infographics, presented the collaborations and their resulting products to scientists, research organizations, and funders via an interactive communications seminar 5 at the “Science Transcending Boundaries” AAAS annual meeting in Washington D.C. in February 2019.

Iterative approach to collaboration

The collaborations typically began with conversations aimed at identifying the target audience . This was surprisingly challenging for a number of the researchers, who wanted to communicate to “the general public”. Because the artists knew that different audiences require different approaches, they challenged the scientists to be as specific as possible. The teams eventually arrived at much more refined audience targets, e.g. “English and Spanish speaking viewers already interested in biodiversity conservation” (Fig.  1 ).

An explanation of why it’s important to protect the structure of plant-pollinator interaction networks. This graphic was designed with bright colors and a minimum of text so that it could be shared on social media. The biggest challenge was finding a way to concisely, yet clearly, explain a high-level abstract topic to biodiversity-interested but non-scientist audiences. The scientist–artist team tried many different approaches before settling on the combination of a news-related hook, a quick graphical summary, and the table metaphor. To reach intended audiences, the graphic was produced both in English and in Spanish. Design by Yael Kisel based on the research of Ignasi Bartomeus [Estación Biológica de Doñana (EBD-CSIC)]

These conversations fed the next step of co-creation, refining the messages of the infographics. In many cases the middle ground had to be found between the scientists’ conviction that the graphics accurately and comprehensively represented the data, and the artists’ emphasis on streamlining the messages to make them easier to understand. Each team had to determine how to distill the research into a communicable story without simplifying to the point that key context was lost. For some, the compromise was found by including data visualizations, to communicate specific information, as well as more abstract designs to relay broader concepts (Fig.  2 ). For others, presentation materials created by the scientists themselves were adapted and further developed into visual components (Supplementary Fig.  1 ).

Two designs from the same infographic focused on the role of small farms in the global food system. a is a data visualization of specific data from the research representing the global geography of small farms. b is a representation of differences in farm size definitions, a concept that the artist thought was more effectively communicated through abstraction. Design by Ellie Barber based on the research of Vincent Ricciardi, Zia Mehrabi, and Navin Ramankutty (University of British Columbia). The full infographic is available in the Dryad Digital Repository

In every case, the process of refining the message and then creating the graphic was iterative , as the teams tried different arrangements of information in search of an effective story. Often the supporting, and even the main, messages changed as the work progressed and as the artists provided input on what they found easy to communicate and on what they thought would be relevant to the target audience. In some cases, the message refinement processes brought forward points that the scientists originally thought were too obvious to mention (Supplementary Fig.  2 ). Colleagues, friends, and family from both the scientists’ and artists’ worlds provided litmus tests for progress. By the end of the project, all of the teams were pleased with their products, which they thought were scientifically accurate, visually appealing, and effectively communicated. All of the infographics are available in the Dryad Digital Repository 6 .

A number of the participating researchers were surprised to find that the act of translating their work into an infographic pushed their science forward. They agonized over the challenge of distilling complex concepts into clear, focused, and accessible messages, but the process helped them to identify the central components of their work and to note areas that they had not studied sufficiently. The process also forced the researchers to reflect on, and then communicate, why they do what they do, as well as how their work impacts society.

Recommendations

As the presentation of science moves beyond the traditional static journal article 7 , there is every reason to think that graphic art will become ever more critical. As a result of our experience, we have developed a set of recommended actions for researchers and their institutions, for graphic art professionals, and for funders, to facilitate productive scientist–artist collaborations (Box  2 ).

Researchers and their institutions should recognize the value of science-graphic art collaborations in improving the communication of research and the accessibility of results relevant to society. The sooner designers are consulted during the research process the better−not only to facilitate the creation of visual media, but because these collaborations improve current and potential future research. Based on the complex research topics of the scientists involved in this project and their uniform response that their work and its communication benefited from these collaborations, we believe that scientists in most, if not all, research areas would similarly benefit. Research societies and journals can support scientist-artist collaborations through promotion and training opportunities.

During the presentation of our project at the AAAS conference, members of the audience asked more than once how they could find a skilled artist to work with. Some organizations contain dedicated arts/design/communications offices that can work with researchers to develop graphics to increase impact (e.g. 8 – 10 ). For scientists without this institutional support, the continued creation and expansion of networks (e.g. 11 ), organizations, and companies (e.g. 12 ) providing these services would be of tremendous value.

Finally, funders should look positively on broader impacts budgets in grant proposals that include resources for graphic design, and should explicitly name graphic design components as broader impacts work they will support. We believe that the relatively limited additional funding needed would provide substantial returns in impact.

Box 1 benefits, applications, and challenges of scientist-graphic artist collaborations

• Better communication of scientific findings
• Increased awareness of research by both experts and non-experts
• Greater impact and reach of science

Applications

• Infographics
• Conference posters
• Graphical abstracts
• Journal article figures
• Journal article covers
• Magazine and newspaper graphics
• Website, blogs, and social media graphics
• Public art pieces and murals
• Scientific, policy, outreach, and educational presentations
• Videos and animations
• Additional time required for collaboration with graphic artists
• Additional project costs to support graphic artists

Box 2 recommendations for fostering scientist–graphic artist collaborations

Researchers and institutions

Promote science-graphic art collaborations by including, engaging, and supporting graphic artists in research projects - both for improved science communications and for the research benefits gained through the iterative collaborative process

Graphic art professionals

Create and expand networks, non-profit organizations, and companies that specialize in producing scientific graphics and/or help researchers to identify artist collaborators

Provide financial support for including graphic artists in funded projects.

Graphics have the potential to increase the attractiveness, understandability, and communication power of research findings. They can help science reach audiences that research literature never will. As such, they are a tremendous asset in a time when the increased politicization of complex scientific issues, such as the future of food and nutrition security, necessitates the communication of science to society in ways are accessible and engaging.

Scientist-artist collaborations can certainly improve traditional research visuals, such as journal figures, presentations, and posters. But applications aimed at reaching broader audiences – online, in print, and on the street – have the potential to do much more (Box 1 ).

As with any multidisciplinary work, such collaborations are not without cost – both in terms of the extra time needed for the iterative process to be productive, and the additional financial resources required to fairly compensate graphic professionals for their contributions. We found that the collaborations necessitated multiple rounds of idea generation and then further concept refinement, but the investment paid off in terms of powerfully communicated graphic art and scientists’ clearer conceptualizations of their own work. In our view, the benefits of scientist-artist collaborations far outweigh their costs – especially as scientific organizations, journals and other media, and funders continue to ask more of researchers with regard to graphics, broader impacts, and public outreach.

Supplementary information

Acknowledgements.

We thank the Leichtag Foundation for providing funding support for the artists involved in the science-graphic design collaborations.

Author contribution

C.K.K., V.R., Z.M., I.B., N.R., A.M., M.S. and M.A.G. provided research material and inputs on the infographic co-creation. Y.K., E.B., L.K., Á.V. and K.N. interpreted the research and co-designed the infographics. N.J. contributed messaging and framing contributions to the scientist-artist collaborations. C.K.K., M.K. and A.N. identified, organized, and managed the scientist-artist collaborations. C.K.K., Y.K., M.K., E.B., V.R., C.K., S.K. and M.A.G. presented the scientist–artist collaborations and infographics at AAAS. C.K.K., Y.K., M.K., E.B., V.R., C.K., L.K., Z.M., N.J., S.K., Á.V., K.N., I.B., N.R., A.M., M.S., M.A.G. and A.N. contributed to writing the commentary.

Data availability

Competing interests.

The authors declare no competing interests.

Publisher’s note: Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Supplementary information accompanies this paper at 10.1038/s42003-019-0516-1.

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Graphic Design in Public Health Research

A multiyear pictorial health warning label initiative and recommendation for sustained interdisciplinary collaboration.

• Michael Schmidt University of Memphis
• Taghrid Asfar University of Miami
• Wasim Maziak Florida International

Graphic design is often deployed in public health research, intervention, and dissemination of information. In some cases, such as the studies shared in this article, graphic design artifacts are the public health intervention, developed and tested within a series of scientific study designs involving research teams with wide-ranging expertise. Relatively little attention has been paid, however, to the role graphic design plays in public health re-search or how graphic designers may contribute to the conduct of research beyond a production services role. Even within the health communication field, the benefits for scientific knowledge and public health emerging from interactions with graphic designers remain understudied. Furthermore, graphic designers have yet to make a substantive case to public health re-searchers that there is more to graphic design than the artifacts it produces. Therefore, the goals of this paper are to 1) provide an overview of methods employed to integrate graphic design into a multiyear series of public health research studies, 2) share key results from these studies relevant to graphic design, and 3) discuss the requirements for sustaining research collaborations between graphic designers and public health researchers in ways that effectively combine their fields of expertise and produce more genuine collaboration for the greater benefit of public health.

Author Biographies

Dr. Michael Schmidt is a public health social and behavioral research sci-entist and graphic designer. He is a professor in the Department of Art and affiliate faculty member in the School of Public Health at The University of Memphis. His past work includes design-based interventions for pediatric informed consent in clinical trials, child development and health, and chil-dren’s rights impact assessment in local, state, and national policy develop-ment. Currently, he is working with two research teams as a co-investigator on several federally funded research grants. His present areas of research include (1) design-as-intervention for smoking cessation and prevention; (2) social and behavioral determinants of substance use disorders, treatment access, and recovery; and (3) domestic violence prevention and interven-tion. Along with his research colleagues, he is a regular contributor to the scientific literature in public health, psychology, and medicine.

Dr. Taghrid Asfar has extensive experience in tobacco control research nationally and internationally. Since 2001, her tobacco control work has been funded continuously by the NIH and conducted both in the United States and the Eastern Mediterranean Region, including Syria, Lebanon, and Tunisia. This work involves epidemiological and qualitative studies of to-bacco use, randomized clinical trials of smoking cessation interventions, and tobacco regulatory research in health communication approaches targeting emerging tobacco products such as e-cigarettes and hookahs. Her research aims are to improve smoking cessation treatment among socially disadvan-taged and high-risk populations and to prevent tobacco use among youth and young adults by advancing health communication strategies. She has more than 60 peer-reviewed publications (Asfar T - Search Results - PubMed (nih.gov)) in high impact journals, including Tobacco Control, Nicotine and Tobacco Research, Addiction, and the Cochrane Tobacco Addiction Group.

Dr. Wasim Maziak is a professor of Epidemiology, Director of the Clinical Research Lab for Tobacco Smoking at Florida International University, and Founder of the Syrian Center for Tobacco Studies. Dr. Maziak has exten-sive experience in tobacco control research and has published over 200 peer-reviewed scientific reports, including contributions in Science, Nature, Lancet, and British Medical Journal. His focus has been on emerging tobacco products such as e-cigarettes and hookah (Waterpipe), especially risk com-munication strategies targeting young users. He has been continuously funded by NIH since 2001 for tobacco control research.

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• What Is a Research Design | Types, Guide & Examples

What Is a Research Design | Types, Guide & Examples

Published on June 7, 2021 by Shona McCombes . Revised on November 20, 2023 by Pritha Bhandari.

A research design is a strategy for answering your   research question  using empirical data. Creating a research design means making decisions about:

• Your overall research objectives and approach
• Whether you’ll rely on primary research or secondary research
• Your sampling methods or criteria for selecting subjects
• Your data collection methods
• The procedures you’ll follow to collect data
• Your data analysis methods

A well-planned research design helps ensure that your methods match your research objectives and that you use the right kind of analysis for your data.

Table of contents

Step 1: consider your aims and approach, step 2: choose a type of research design, step 3: identify your population and sampling method, step 4: choose your data collection methods, step 5: plan your data collection procedures, step 6: decide on your data analysis strategies, other interesting articles, frequently asked questions about research design.

• Introduction

Before you can start designing your research, you should already have a clear idea of the research question you want to investigate.

There are many different ways you could go about answering this question. Your research design choices should be driven by your aims and priorities—start by thinking carefully about what you want to achieve.

The first choice you need to make is whether you’ll take a qualitative or quantitative approach.

Qualitative research designs tend to be more flexible and inductive , allowing you to adjust your approach based on what you find throughout the research process.

Quantitative research designs tend to be more fixed and deductive , with variables and hypotheses clearly defined in advance of data collection.

It’s also possible to use a mixed-methods design that integrates aspects of both approaches. By combining qualitative and quantitative insights, you can gain a more complete picture of the problem you’re studying and strengthen the credibility of your conclusions.

Practical and ethical considerations when designing research

As well as scientific considerations, you need to think practically when designing your research. If your research involves people or animals, you also need to consider research ethics .

• How much time do you have to collect data and write up the research?
• Will you be able to gain access to the data you need (e.g., by travelling to a specific location or contacting specific people)?
• Do you have the necessary research skills (e.g., statistical analysis or interview techniques)?
• Will you need ethical approval ?

At each stage of the research design process, make sure that your choices are practically feasible.

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Within both qualitative and quantitative approaches, there are several types of research design to choose from. Each type provides a framework for the overall shape of your research.

Types of quantitative research designs

Quantitative designs can be split into four main types.

• Experimental and   quasi-experimental designs allow you to test cause-and-effect relationships
• Descriptive and correlational designs allow you to measure variables and describe relationships between them.

With descriptive and correlational designs, you can get a clear picture of characteristics, trends and relationships as they exist in the real world. However, you can’t draw conclusions about cause and effect (because correlation doesn’t imply causation ).

Experiments are the strongest way to test cause-and-effect relationships without the risk of other variables influencing the results. However, their controlled conditions may not always reflect how things work in the real world. They’re often also more difficult and expensive to implement.

Types of qualitative research designs

Qualitative designs are less strictly defined. This approach is about gaining a rich, detailed understanding of a specific context or phenomenon, and you can often be more creative and flexible in designing your research.

The table below shows some common types of qualitative design. They often have similar approaches in terms of data collection, but focus on different aspects when analyzing the data.

Your research design should clearly define who or what your research will focus on, and how you’ll go about choosing your participants or subjects.

In research, a population is the entire group that you want to draw conclusions about, while a sample is the smaller group of individuals you’ll actually collect data from.

Defining the population

A population can be made up of anything you want to study—plants, animals, organizations, texts, countries, etc. In the social sciences, it most often refers to a group of people.

For example, will you focus on people from a specific demographic, region or background? Are you interested in people with a certain job or medical condition, or users of a particular product?

The more precisely you define your population, the easier it will be to gather a representative sample.

• Sampling methods

Even with a narrowly defined population, it’s rarely possible to collect data from every individual. Instead, you’ll collect data from a sample.

To select a sample, there are two main approaches: probability sampling and non-probability sampling . The sampling method you use affects how confidently you can generalize your results to the population as a whole.

Probability sampling is the most statistically valid option, but it’s often difficult to achieve unless you’re dealing with a very small and accessible population.

For practical reasons, many studies use non-probability sampling, but it’s important to be aware of the limitations and carefully consider potential biases. You should always make an effort to gather a sample that’s as representative as possible of the population.

Case selection in qualitative research

In some types of qualitative designs, sampling may not be relevant.

For example, in an ethnography or a case study , your aim is to deeply understand a specific context, not to generalize to a population. Instead of sampling, you may simply aim to collect as much data as possible about the context you are studying.

In these types of design, you still have to carefully consider your choice of case or community. You should have a clear rationale for why this particular case is suitable for answering your research question .

For example, you might choose a case study that reveals an unusual or neglected aspect of your research problem, or you might choose several very similar or very different cases in order to compare them.

Data collection methods are ways of directly measuring variables and gathering information. They allow you to gain first-hand knowledge and original insights into your research problem.

You can choose just one data collection method, or use several methods in the same study.

Survey methods

Surveys allow you to collect data about opinions, behaviors, experiences, and characteristics by asking people directly. There are two main survey methods to choose from: questionnaires and interviews .

Observation methods

Observational studies allow you to collect data unobtrusively, observing characteristics, behaviors or social interactions without relying on self-reporting.

Observations may be conducted in real time, taking notes as you observe, or you might make audiovisual recordings for later analysis. They can be qualitative or quantitative.

Other methods of data collection

There are many other ways you might collect data depending on your field and topic.

If you’re not sure which methods will work best for your research design, try reading some papers in your field to see what kinds of data collection methods they used.

Secondary data

If you don’t have the time or resources to collect data from the population you’re interested in, you can also choose to use secondary data that other researchers already collected—for example, datasets from government surveys or previous studies on your topic.

With this raw data, you can do your own analysis to answer new research questions that weren’t addressed by the original study.

Using secondary data can expand the scope of your research, as you may be able to access much larger and more varied samples than you could collect yourself.

However, it also means you don’t have any control over which variables to measure or how to measure them, so the conclusions you can draw may be limited.

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As well as deciding on your methods, you need to plan exactly how you’ll use these methods to collect data that’s consistent, accurate, and unbiased.

Planning systematic procedures is especially important in quantitative research, where you need to precisely define your variables and ensure your measurements are high in reliability and validity.

Operationalization

Some variables, like height or age, are easily measured. But often you’ll be dealing with more abstract concepts, like satisfaction, anxiety, or competence. Operationalization means turning these fuzzy ideas into measurable indicators.

If you’re using observations , which events or actions will you count?

If you’re using surveys , which questions will you ask and what range of responses will be offered?

You may also choose to use or adapt existing materials designed to measure the concept you’re interested in—for example, questionnaires or inventories whose reliability and validity has already been established.

Reliability and validity

Reliability means your results can be consistently reproduced, while validity means that you’re actually measuring the concept you’re interested in.

For valid and reliable results, your measurement materials should be thoroughly researched and carefully designed. Plan your procedures to make sure you carry out the same steps in the same way for each participant.

If you’re developing a new questionnaire or other instrument to measure a specific concept, running a pilot study allows you to check its validity and reliability in advance.

Sampling procedures

As well as choosing an appropriate sampling method , you need a concrete plan for how you’ll actually contact and recruit your selected sample.

That means making decisions about things like:

• How many participants do you need for an adequate sample size?
• What inclusion and exclusion criteria will you use to identify eligible participants?
• How will you contact your sample—by mail, online, by phone, or in person?

If you’re using a probability sampling method , it’s important that everyone who is randomly selected actually participates in the study. How will you ensure a high response rate?

If you’re using a non-probability method , how will you avoid research bias and ensure a representative sample?

Data management

It’s also important to create a data management plan for organizing and storing your data.

Will you need to transcribe interviews or perform data entry for observations? You should anonymize and safeguard any sensitive data, and make sure it’s backed up regularly.

Keeping your data well-organized will save time when it comes to analyzing it. It can also help other researchers validate and add to your findings (high replicability ).

On its own, raw data can’t answer your research question. The last step of designing your research is planning how you’ll analyze the data.

Quantitative data analysis

In quantitative research, you’ll most likely use some form of statistical analysis . With statistics, you can summarize your sample data, make estimates, and test hypotheses.

Using descriptive statistics , you can summarize your sample data in terms of:

• The distribution of the data (e.g., the frequency of each score on a test)
• The central tendency of the data (e.g., the mean to describe the average score)
• The variability of the data (e.g., the standard deviation to describe how spread out the scores are)

The specific calculations you can do depend on the level of measurement of your variables.

Using inferential statistics , you can:

• Make estimates about the population based on your sample data.
• Test hypotheses about a relationship between variables.

Regression and correlation tests look for associations between two or more variables, while comparison tests (such as t tests and ANOVAs ) look for differences in the outcomes of different groups.

Your choice of statistical test depends on various aspects of your research design, including the types of variables you’re dealing with and the distribution of your data.

Qualitative data analysis

In qualitative research, your data will usually be very dense with information and ideas. Instead of summing it up in numbers, you’ll need to comb through the data in detail, interpret its meanings, identify patterns, and extract the parts that are most relevant to your research question.

Two of the most common approaches to doing this are thematic analysis and discourse analysis .

There are many other ways of analyzing qualitative data depending on the aims of your research. To get a sense of potential approaches, try reading some qualitative research papers in your field.

If you want to know more about the research process , methodology , research bias , or statistics , make sure to check out some of our other articles with explanations and examples.

• Simple random sampling
• Stratified sampling
• Cluster sampling
• Likert scales
• Reproducibility

 Statistics

• Null hypothesis
• Statistical power
• Probability distribution
• Effect size
• Poisson distribution

Research bias

• Optimism bias
• Cognitive bias
• Implicit bias
• Hawthorne effect
• Anchoring bias
• Explicit bias

A research design is a strategy for answering your   research question . It defines your overall approach and determines how you will collect and analyze data.

A well-planned research design helps ensure that your methods match your research aims, that you collect high-quality data, and that you use the right kind of analysis to answer your questions, utilizing credible sources . This allows you to draw valid , trustworthy conclusions.

Quantitative research designs can be divided into two main categories:

• Correlational and descriptive designs are used to investigate characteristics, averages, trends, and associations between variables.
• Experimental and quasi-experimental designs are used to test causal relationships .

Qualitative research designs tend to be more flexible. Common types of qualitative design include case study , ethnography , and grounded theory designs.

The priorities of a research design can vary depending on the field, but you usually have to specify:

• Your research questions and/or hypotheses
• Your overall approach (e.g., qualitative or quantitative )
• The type of design you’re using (e.g., a survey , experiment , or case study )
• Your data collection methods (e.g., questionnaires , observations)
• Your data collection procedures (e.g., operationalization , timing and data management)
• Your data analysis methods (e.g., statistical tests  or thematic analysis )

A sample is a subset of individuals from a larger population . Sampling means selecting the group that you will actually collect data from in your research. For example, if you are researching the opinions of students in your university, you could survey a sample of 100 students.

In statistics, sampling allows you to test a hypothesis about the characteristics of a population.

Operationalization means turning abstract conceptual ideas into measurable observations.

For example, the concept of social anxiety isn’t directly observable, but it can be operationally defined in terms of self-rating scores, behavioral avoidance of crowded places, or physical anxiety symptoms in social situations.

Before collecting data , it’s important to consider how you will operationalize the variables that you want to measure.

A research project is an academic, scientific, or professional undertaking to answer a research question . Research projects can take many forms, such as qualitative or quantitative , descriptive , longitudinal , experimental , or correlational . What kind of research approach you choose will depend on your topic.

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The Significance of Design Research in Graphic Designing

Research in graphic design gives you the answers to critical questions like what characteristics should a logo, web page, poster, etc. have? 

When we say design research, we don’t mean scientific experiments and analysis. We simply mean gaining a more nuanced understanding of:

• The people, business, or brand you are designing for
• The basic questions that come up during the design process. 

At the end of the research, you may not have hard facts in your hand, such as which colour to use or not. But you will have feasible ideas and concepts upon which you can build to make effective and stunning designs. 

This brings us to the question of what is design research. We unpack what it means in this blog. We also talk about the benefits of research in graphic designing and the golden rules you should follow. 

What is Research in Graphic Design?

Research is critical in any area of design, from logos to posters. It is about collecting data through interviews with clients, user feedback, domain research and more. It guides the creation of the design by helping you understand what makes people tick. 

It lends you a frame of reference using which you can shape an idea into something people will want to see. At the very heart of it, design research is learning about people’s behaviour. 

Let’s say you are designing a website. With research, you find out who will visit the site and what sort of design elements will appeal to them the most. 

Why must a graphic designer do research?

There are several reasons why every good graphic design course emphasizes on research:

• It helps you fully understand the problem.
• It helps you create a design with confidence. 
• What do you need to design?
• Why do you need to make it?
• How will it be used?

Essentially, research arms a graphic designer with knowledge, making it easier to create something that the client will love. 

Assume you are asked to make a UI. Without research, the chances of the UI design being rejected are high. Why? Because you don’t have enough information on what will appeal to the client. 

On the other hand, if you research well, you’ll design a UI that will work in the real world and be liked by people. This increases the chances of the client accepting your design. 

But can’t a graphic designer rely on the information the client provided? No. You need to ask questions, dig deeper to understand the industry, the company, the product or service. 

A graphic designer can never have too much information. This is particularly important when designing for an industry, product or service you are not aware of. 

What are the benefits of research in graphic design?

Design research is a mix of aesthetics, user feedback, technology, and the client’s goals. But the most effective graphic designs are user-centric.

A user-centric approach to graphic design means you keep the people who will see the design as the most important factor. How do you do that? By gathering feedback from users. 

You can conduct interviews with the target audience to determine what resonates with them and what parts of the design they do not like. You can even talk to the brand, client or business to find out what they want to communicate with the design. 

In simpler words, the biggest benefit of design research is unearthing crucial information on what users want and what the brand hopes to accomplish . 

A very simple example of research in design is creating mood boards. You can create a mood board for colour, iconography, typography and more. You can then show them to others to test what rings a bell with the client and aligns with their goals. Using the feedback, you can then refine your design. 

It helps you uncover actionable insights.

Graphic design is complex. There are too many aspects that go into designing something even as simple as a logo. 

That’s the second benefit of research in graphic designing.  It helps you identify the client’s tastes and preferences before you make a heavy investment into the design. 

For instance, you are asked to make a web page for a cosmetic company. How do you pin down what type of layout they will like? Or if they like bold colours and vibrant images instead of subtle colour and simple photos?

Adding to the confusion is the fact that the same thing may have different meanings for people. What appears “cool” to you may seem “not sophisticated enough” to the cosmetic company.

Design research helps you remove mix-ups like these. You get clear cut insights that allow you to take action. 

What are the 5 golden rules of design research?

As the best graphic design institute in Kolkata and our experience with innumerable graphic designers says there are 5 thumb rules in design research.

Art not science

Design research is not a science experiment that will give you numbers. It is more an art. What you should be looking for is what emotions does the design evoke , does it resonate with people, or does it intrigue their sensibilities? 

Perception over preference

Don’t focus your research on what type of design people prefer. Instead of asking if this design will sway a person to buy a product, ask what it communicates to them or how they perceive it.

Brand comes first

Graphic designers often confuse research with asking people to explain what they like in their design. That should not be the focal point. It should be the brand. First, ask people what they love about the product or service. Then ask them if the design communicates the same or not. 

Factor in familiarity

People do not like change. So, they tend to like what is familiar to them. If you design something disruptive, ground-breaking or new, keep in mind that most people may not like it at first glance . 

Don’t ask for advice

You are the graphic designer, not the consumer. So, never ask them for advice on how to improve the logo, webpage, poster or more. Simply pay attention to how they react to the design and not their so-called expertise.

What should graphic designer research? 

Now that you are clear on what design research is, how it benefits you and what rules you must follow, we give you the five key areas where you should begin your research. 

Since graphic design research can be a broad area, we use logo designing as an example. But you can use the process for design research for any other field.

Step 1: why do they need a logo?

This should always be the first step – finding out why the brand or company needs the design. In the case of a logo, it can be because they are a new brand or they may be redesigning.

For a new company, your research should move on to step two. But if it is a redesign, dig deeper.

• Is the company redesigning because their original logo was created in a hurry (and cheaply) when they started?
• Are they creating a new product, and that is why they need a new logo?
• Are they merging with another company (like in the case of Vodafone and Idea) and require a different logo?

You have to understand the reasons for the change in the logo . Only then will you be able to decide on whether you need to start from scratch or to evolve the current version. 

Step 2: what is the brand about?

It sounds like common sense, but plenty of graphic designers skip this step. Please don’t. You need to know what the brand or company does . Also, discover:

• The history of the firm
• What products do they have?
• What problem does the product solve?
• What are the values of the brand?
• What message does the company want to communicate?

The answers to all this should impact the design of the logo. 

Step 3: Who is the audience?

How do you design an attractive logo? By knowing the target audience . No matter how striking your logo is, it will not be effective if the intended audience is kids, and you designed it for adults. 

One way to research the target audience is to ask the client. If they don’t know who they want to target, ask them to describe their ideal customer. This would include:

• What is their gender?
• What is their age?
• Where are they located?
• What is their lifestyle?
• How much do they earn?

Knowing these demographics will help you understand their pain points and what they want from a brand. Use it to design the logo. 

Step 4: What is the company’s long-term vision?

A logo lasts for decades and decades. It should remain relevant even 45 (or more) years later. That’s why it is pivotal to know where the company sees itself in the long term. 

Say you’re designing the logo for a sports shoe company. But 10 years down the line, they hope to expand to apparel and sports equipment. You’ll need to consider this when creating the logo. 

How do you research the long-term goals of the brand? You ask the client. Question them about their future plans. Based on the answer, design the logo. 

Step 5: Who is their competitor?

The last thing a graphic designer should research is the competition. It will assist you in:

• Identifying the intended audience
• Discovering what not to use in the logo.

But more importantly, it will prevent you from making a grave mistake – unknowingly creating a logo that looks like a competitor’s . While it is tempting to copy well-known logos, it is never effective. You want the design to stand out and be memorable.  

How do you research competitors of the brand? One, you ask the client to give you a list. Two, do your own search on Google. Look at companies that sell the same service or product and companies that sell something similar. 

Where to start learning research in graphic design?

The value of research is undeniable in every field. It gives you indispensable information. That knowledge guides you to better practice. But more than that, research can nurture innovation along with creative aptitude. 

The belief that graphic design is only art and needs no research is, therefore, inaccurate. It doesn’t stifle creativity; neither does it scare you from designing something revolutionary. Rather it allows you to explore deeper and further.

It removes the guesswork from your design. It takes care of any miscommunication between the graphic designer and the rest of the team. In short, research is a powerful tool in graphic design. 

Use it, and you become a better graphic designer because you now have knowledge of audiences and competitors. Besides, it can help you discover the latest trends in graphic design . 

That leaves you with just one question – where do you learn design research? You join a graphic design course that pays particular attention to research and concept building. Any of the best graphic design institutes near you would be a great place to start!

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REVIEW article

Eeg-based study of design creativity: a review on research design, experiments, and analysis.

• Concordia Institute for Information Systems Engineering, Gina Cody School of Engineering and Computer Science, Concordia University, Montreal, QC, Canada

Brain dynamics associated with design creativity tasks are largely unexplored. Despite significant strides, there is a limited understanding of the brain-behavior during design creation tasks. The objective of this paper is to review the concepts of creativity and design creativity as well as their differences, and to explore the brain dynamics associated with design creativity tasks using electroencephalography (EEG) as a neuroimaging tool. The paper aims to provide essential insights for future researchers in the field of design creativity neurocognition. It seeks to examine fundamental studies, present key findings, and initiate a discussion on associated brain dynamics. The review employs thematic analysis and a forward and backward snowball search methodology with specific inclusion and exclusion criteria to select relevant studies. This search strategy ensured a comprehensive review focused on EEG-based creativity and design creativity experiments. Different components of those experiments such as participants, psychometrics, experiment design, and creativity tasks, are reviewed and then discussed. The review identifies that while some studies have converged on specific findings regarding EEG alpha band activity in creativity experiments, there remain inconsistencies in the literature. The paper underscores the need for further research to unravel the interplays between these cognitive processes. This comprehensive review serves as a valuable resource for readers seeking an understanding of current literature, principal discoveries, and areas where knowledge remains incomplete. It highlights both positive and foundational aspects, identifies gaps, and poses lingering questions to guide future research endeavors.

1 Introduction

1.1 creativity, design, and design creativity.

Investigating design creativity presents significant challenges due to its multifaceted nature, involving nonlinear cognitive processes and various subtasks such as divergent and convergent thinking, perception, memory retrieval, learning, inferring, understanding, and designing ( Gero, 1994 ; Gero, 2011 ; Nguyen and Zeng, 2012 ; Jung and Vartanian, 2018 ; Xie, 2023 ). Additionally, design creativity tasks are often ambiguous, intricate, and nonlinear, further complicating efforts to understand the underlying mechanisms and the brain dynamics associated with creative design processes.

Creativity, one of the higher-order cognitive processes, is defined as the ability to develop useful, novel, and surprising ideas ( Sternberg and Lubart, 1998 ; Boden, 2004 ; Runco and Jaeger, 2012 ; Simonton, 2012 ). Needless to say, creativity occurs in all parts of social and personal life and all situations and places, including everyday cleverness, the arts, sciences, business, social interaction, and education ( Mokyr, 1990 ; Cropley, 2015b ). However, this study particularly focuses on reviewing EEG-based studies of creativity and design creativity tasks.

Design, as a fundamental and widespread human activity, aiming at changing existing situations into desired ones ( Simon, 1996 ), is nonlinear and complex ( Zeng, 2001 ), and lies at the heart of creativity ( Guilford, 1959 ; Gero, 1996 ; Jung and Vartanian, 2018 ; Xie, 2023 ). According to the recursive logic of design ( Zeng and Cheng, 1991 ), a designer intensively interacts with the design problem, design environment (including stakeholders of design, design context, and design knowledge), and design solutions in the recursive environment-based design evolution process ( Zeng and Gu, 1999 ; Zeng, 2004 , 2015 ; Nagai and Gero, 2012 ). Zeng (2002) conceptualized the design process as an environment-changing process in which the product emerges from the environment, serves the environment, and changes the environment ( Zeng, 2015 ). Convergent and divergent thinking are two primary modes of thinking in the design process, which are involved in analytical, critical, and synthetic processes. Divergent thinking leads to possible solutions, some of which might be creative, to the design problem whereas convergent thinking will evaluate and filter the divergent solutions to choose appropriate and practical ones ( Pahl et al., 1988 ).

Creative design is inherently unpredictable; at times, it may seem implausible – yet it happens. Some argue that a good design process and methodology form the foundation of creative design, while others emphasize the significance of both design methodology and knowledge in fostering creativity. It is noteworthy that different designers may propose varied solutions to the same design problem, and even the same designer might generate diverse design solutions for the same problem over time ( Zeng, 2001 ; Boden, 2004 ). Creativity may spontaneously emerge even if one does not intend to conduct a creative design, whereas creative design just may not come out no matter how hard one tries. A design is considered routine if it operates within a design space of known and ordinary designs, innovative if it navigates within a defined state space of potential designs but yields different outcomes, and creative if it introduces new variables and structures into the space of potential designs ( Gero, 1990 ). Moreover, it is conceivable that a designer may lack creativity while the product itself demonstrates creative attributes, and conversely, a designer may exhibit creativity while the resulting product does not ( Yang et al., 2022 ).

Several models of design creativity have been proposed in the literature. In some earlier studies, design creativity was addressed as engineering creativity or creative problem-solving ( Cropley, 2015b ). Used in recent studies ( Jia et al., 2021 ; Jia and Zeng, 2021 ), the stages of design creativity include problem understanding, idea generation, idea evolution, and idea validation ( Guilford, 1959 ). Problem understanding and idea evaluation are assumed to be convergent cognitive tasks whereas idea generation and idea evolution are considered divergent tasks in design creativity. An earlier model of creative thinking proposed by Wallas (1926) is presented in four phases including preparation, incubation, illumination, and verification ( Cropley, 2015b ). The “Preparation” phase involves understanding a topic and defining the problem. During “Incubation,” one processes the information, usually subconsciously. In the “Illumination” phase, a solution appears, often unexpectedly. Lastly, “Verification” involves evaluating and implementing the derived solution. In addition to this model, a seven-phase model (an extended version of the 4-phase model) was later introduced containing preparation, activation, generation, illumination, verification, communication, and validation ( Cropley, 2015a , b ). It is crucial to emphasize that these phases are not strictly sequential or distinct in that interactions, setbacks, restarts, or premature conclusions might occur ( Haner, 2005 ). In contrast to those emperical models of creativity, the nonlinear recursive logic of design creativity was rigorously formalized in a mathematical design creativity theory ( Zeng, 2001 ; Zeng et al., 2004 ; Zeng and Yao, 2009 ; Nguyen and Zeng, 2012 ). For further details on the theories and models of creativity and design creativity, readers are directed to the referenced literature ( Gero, 1994 , 2011 ; Kaufman and Sternberg, 2010 ; Williams et al., 2011 ; Nagai and Gero, 2012 ; Cropley (2015b) ; Jung and Vartanian, 2018 ; Yang et al., 2022 ; Xie, 2023 ).

1.2 Design creativity neurocognition

First, we would like to provide the definitions of “design” and “creativity” which can be integrated into the definition of “design creativity.” According to the Cambridge Dictionary, the definition of design is: “to make or draw plans for something.” In addition, the definition of creativity is: “the ability to make something new or imaginative.” So, the definition of design creativity is: “the ability to design something new and valuable.” With these definitions, we focus on design creativity neurocognition in this section.

It is of great importance to study design creativity neurocognition as the brain plays a pivotal role in the cognitive processes underlying design creativity tasks. So, to better investigate design creativity we need to concentrate on brain mechanisms associated with the related cognitive processes. However, the complexity of these tasks has led to a significant gap in our understanding; consequently, our knowledge about the neural activities associated with design creativity remains largely limited and unexplored. To address this gap, a burgeoning field known as design creativity neurocognition has emerged. This field focuses on investigating the intricate and unstructured brain dynamics involved in design creativity using various neuroimaging tools such as electroencephalography (EEG).

In a nonlinear evolutionary model of design creativity, it is suggested that the brain handles problems and ideas in a way that leads to unpredictable and potentially creative solutions ( Zeng, 2001 ; Nguyen and Zeng, 2012 ). This involves cognitive processes like thinking of ideas, evolving and evaluating them, along with physical actions like drawing ( Zeng et al., 2004 ; Jia, 2021 ). This indicates that the brain, as a complex and nonlinear system with characteristics like emergence and self-organization, goes through several cognitive processes which enable the generation of creative ideas and solutions. Exploring brain activities during design creativity tasks helps us get a better insight into the design process and improves how designers perform. As a result, design neurocognition combines traditional design study methods with approaches from cognitive neuroscience, neurophysiology, and artificial intelligence, offering unique perspectives on understanding design thinking ( Balters et al., 2023 ). Although several studies have focused on design and creativity, brain dynamics associated with design creativity are largely untouched. It motivated us to conduct this literature review to explore the studies, gather the information and findings, and finally discuss them. Due to the advantages of electroencephalography (EEG) in design creativity experiments which will be explained in the following paragraphs, we decided to focus on EEG-based neurocognition in design creativity.

As mentioned before, design creativity tasks are cognitive activities which are complex, dynamic, nonlinear, self-organized, and emergent. The brain dynamics of design creativity are largely unknown. Brain behavior recognition during design-oriented tasks helps scientists investigate neural mechanisms, vividly understand design tasks, enhance whole design processes, and better help designers ( Nguyen and Zeng, 2014a , b , 2017 ; Liu et al., 2016 ; Nguyen et al., 2018 , 2019 ; Zhao et al., 2018 , 2020 ; Jia, 2021 ; Jia et al., 2021 ; Jia and Zeng, 2021 ). Exploring brain neural circuits in design-related processes has recently gained considerable attention in different fields of science. Several studies have been conducted to decode brain activity in different steps of design creativity ( Petsche et al., 1997 ; Nguyen and Zeng, 2010 , 2014a , b , 2017 ; Liu et al., 2016 ; Nguyen et al., 2018 ; Vieira et al., 2019 ). Such attempts will lead to investigating the mechanism and nature of the design creativity process and consequently enhance designers’ performance ( Balters et al., 2023 ). The main question of the studies performed in design creativity neurocognition is whether and how we can explore brain dynamics and infer designers’ cognitive states using neuro-cognitive and physiological data like EEG signals.

Neuroimaging is a vital tool in understanding the brain’s structure and function, offering insights into various neurological and psychological conditions. It employs a range of techniques to visualize the brain’s activity and structure. Neuroimaging methods mainly include magnetic resonance imaging (MRI), computed tomography (CT), electroencephalography (EEG), functional near-infrared spectroscopy (fNIRS), functional MRI (fMRI), and magnetoencephalography (MEG). Neuroimaging techniques have helped researchers explore brain dynamics in complex cognitive tasks, one of which is design creativity ( Nguyen and Zeng, 2014b ; Gao et al., 2017 ; Zhao et al., 2020 ). While several neuroimaging methods exist to study brain activity, electroencephalography (EEG) is one of the best methods which has been widely used in several studies in different applications. EEG, as an inexpensive and simple neuroimaging technique with a high temporal resolution and an acceptable spatial resolution, has been used to infer designers’ cognitive and emotional states. Zangeneh Soroush et al. (2023a , b) have recently introduced two comprehensive datasets encompassing EEG recordings in design and creativity experiments, stemmed from several EEG-based design and design creativity studies ( Nguyen and Zeng, 2014a ; Nguyen et al., 2018 , 2019 ; Jia, 2021 ; Jia et al., 2021 ; Jia and Zeng, 2021 ). In this paper, we review some of the most fundamental studies which have employed electroencephalography (EEG) to explore brain behavior in creativity and design creativity tasks.

1.3 EEG approach to studying creativity neurocognition

EEG stands out as a highly promising method for investigating brain dynamics across various fields, including cognitive, clinical, and computational neuroscience studies. In the context of design creativity, EEG offers a valuable means to explore brain activity, particularly considering the physical movements inherent in the design process. However, EEG analysis poses challenges due to its complexity, nonlinearity, and susceptibility to various artifacts. Therefore, gaining a comprehensive understanding of EEG and mastering its utilization and processing is crucial for conducting effective experiments in design creativity research. This review aims to examine studies that have utilized EEG in investigating design creativity tasks.

EEG is a technique for recording the electrical activity of the brain, primarily generated by neuronal firing within the human brain. This activity is almost always captured non-invasively from the scalp in most cognitive studies, though intracranial EEG (iEEG) is recorded inside the skull, for instance in surgical planning for epilepsy. EEG signals are the result of voltage differences measured across two points on the scalp, reflecting the summed synchronized synaptic activities of large populations of cortical neurons, predominantly from pyramidal cells ( Teplan, 2002 ; Sanei and Chambers, 2013 ).

While the spatial resolution of EEG is relatively poor, EEG offers excellent temporal resolution, capturing neuronal dynamics within milliseconds, a feature not matched by other neuroimaging modalities like functional Near-Infrared Spectroscopy (fNIRS), Positron Emission Tomography (PET), or functional Magnetic Resonance Imaging (fMRI).

In contrast, fMRI provides much higher spatial resolution, offering detailed images of brain activity by measuring blood flow changes associated with neuronal activity. However, fMRI’s temporal resolution is lower than EEG, as hemodynamic responses are slower than electrical activities. PET, like fMRI, offers high spatial resolution and involves tracking a radioactive tracer injected into the bloodstream to image metabolic processes in the brain. It is particularly useful for observing brain metabolism and neurochemical changes but is invasive and has limited temporal resolution. fNIRS, measuring hemodynamic responses in the brain via near-infrared light, stands between EEG and fMRI in terms of spatial resolution. It is non-invasive and offers better temporal resolution than fMRI but is less sensitive to deep brain structures compared to fMRI and PET. Each of these techniques, with their unique strengths and limitations, provides complementary insights into brain function ( Baillet et al., 2001 ; Sanei and Chambers, 2013 ; Choi and Kim, 2018 ; Peng, 2019 ).

This understanding of EEG, from its historical development by Hans Berger in 1924 to modern digital recording and analysis techniques, underscores its significance in studying brain function and diagnosing neurological conditions. Despite advancements in technology, the fundamental methods of EEG recording have remained largely unchanged, emphasizing its enduring relevance in neuroscience ( Teplan, 2002 ; Choi and Kim, 2018 ).

1.4 Objectives and structure of the paper

Balters et al. (2023) conducted a comprehensive systematic review including 82 papers on design neurocognition covering nine topics and a large variety of methodological approaches in design neurocognition. A systematic review ( Pidgeon et al., 2016 ), reported several EEG-based studies on functional neuroimaging of visual creativity. Although such a comprehensive review exists in the field of design neurocognition, just a few early reviews focused on creativity neurocognition ( Fink and Benedek, 2014 , 2021 ; Benedek and Fink, 2019 ).

The present review not only reports the studies but also critically discusses the previous findings and results. The rest of this paper is organized as follows: Section 2 introduces our review methodology; Section 3 presents the results from our review process, and Section 4 discusses the major implications of the existing design creativity neurocognition research in future studies. Section 5 concludes the paper.

2 Methods and materials

Figure 1 shows the main components of EEG-based design creativity studies: (1) experiment design, (2) participants, (3) psychometric tests, (4) experiments (creativity tasks), (5) EEG recording and analysis methods, and (6) final data analysis. The experiment design consists of experiment protocol which includes (design) creativity tasks, the criteria to choose participants, the conditions of the experiment, and recorded physiological responses (which is EEG here). Setting and adjusting these components play a crucial role in successful experiments and reliable results. In this paper, we review studies based on the components in Figure 1 .

Figure 1 . The main components of EEG-based design creativity studies.

The components described in Figure 1 are consistent with the stress-effort model proposed by Nguyan and Zeng ( Nguyen and Zeng, 2012 ; Zhao et al., 2018 ; Yang et al., 2021 ) which characterizes the relationship between mental stress and mental effort by a bell-shaped curve. This model defines mental stress as a ratio of the perceived task workload over the mental capability constituted by affect, skills, and knowledge. Knowledge is shaped by individual experience and understanding related to the given task workload. Skills encompass thinking styles, strategies, and reasoning ability. The degree of affect in response to a task workload can influence the effective utilization of the skills and knowledge. We thus used this model to form our research questions, determine the keywords, and conduct our analysis and discussions.

2.1 Research questions

We focused on the studies assessing brain function in design creativity experiments through EEG analysis. For a comprehensive review, we followed a thorough search strategy, called thematic analysis ( Braun and Clarke, 2012 ), which helped us to code and extract themes from the initial (seed) papers. We began without a fixed topic, immersing ourselves in the existing literature to shape our research questions, keywords, and search queries. Our research questions formed the search keywords and later the search inquiries.

Our main research questions (RQs) were:

RQ1: What are the effective experiment design and protocol to ensure high-quality EEG-based design creativity studies?

RQ2: How can we efficiently record, preprocess, and process EEG reflecting the cognitive workload associated with design creativity tasks?

RQ3: What are the existing methods to analyze the data extracted from EEG signals recorded during design creativity tasks?

RQ4: How can EEG signals provide significant insight into neural circuits and brain dynamics associated with design creativity tasks?

RQ5: What are the significant neuroscientific findings, shortcomings, and inconsistencies in the literature?

With the initial information extracted from the seed papers and the previous studies by the authors in this field ( Nguyen and Zeng, 2012 , 2014a , b ; Jia et al., 2021 ; Jia and Zeng, 2021 ; Yang et al., 2022 ; Zangeneh Soroush et al., 2024 ), we built a conceptual model represented by Figure 1 and then formed these research questions. With this understanding and the RQs, we set our search strategy.

2.2 Search strategy and inclusion-exclusion criteria

Our search started with broad terms like “design,” “creativity,” and “EEG.” These terms encapsulate the overarching cognitive activities and physiological measurement. As we identified relevant papers, we refined our search keywords for a more targeted search. We utilized the Boolean operators such as “OR” and “AND” to finetune our search inquiries. The search inquiries were enhanced by the authors through the knowledge they obtained through selected papers. The first phase started with thematic analysis and continued with choosing papers, obtaining knowledge, discussing the keywords, and updating the search inquiries, recursively until reaching an appropriate search inquiry which resulted in the desired search results. We applied the thematic analysis only in the first iteration to make sure that we had the right and comprehensive understanding of EEG-based design creativity, the appropriate set of keywords, and search inquiries. Finally, we came up with a comprehensive search inquiry as follows:

(“EEG” OR “Electroenceph*” OR “brain” OR “neur*” OR “neural correlates” OR “cognit*”) AND (“design creativity” OR “ideation” OR “creative” OR “divergent thinking” OR “convergent thinking” OR “design neurocognition” OR “creativity” OR “creative design” OR “design thinking” OR “design cognition” OR “creation”)

The search inquiry is a combination of terminologies related to design and creativity, as well as terminologies about EEG, neural activity, and the brain. In a general and quick evaluation, we found out that our proposed search inquiry resulted in relevant studies in the field. This evaluation was a quick way to check how effectively our search keywords work. Then, we went through well-known databases such as PubMed, Scopus, and Web of Science to collect a comprehensive set of original papers, theses, and reviews. These electronic databases were searched to reduce the risk of bias, to get more accurate findings, and to provide coverage of the literature. We continued our search in the aforementioned databases until no more significant papers emerged from those specific databases. It is worth mentioning that we do not consider any specific time interval in our search procedure. We used the fields “title,” “abstract,” and “keywords” in our search process. Then, we selected the papers based on the following inclusion criteria:

1. The paper should answer one or more research questions (RQ1-RQ5).

2. The paper must be a peer-reviewed journal paper authored in English.

3. The paper should focus on EEG analysis related to creativity or design creativity for adult participants.

4. The paper should be related to creativity or design creativity in terms of the concepts, experiments, protocols, and probable models employed in the studies.

5. The paper should use established creativity tasks, including the Alternative Uses Task (AUT), the Torrance Tests of Creative Thinking (TTCT), or a specific design task. (These tasks will be detailed further on.)

6. The paper should include a quantitative analysis of EEG signals in the creativity or design creativity domain.

7. In addition to the above-mentioned criteria, the authors checked the papers to make sure that the included publications have the characteristics of high-quality papers.

These criteria were used to select our initial papers from the large set of papers we collected from Scopus, Web of Science, and PubMed. It should be mentioned that conflicts were resolved through discussion and duplicate papers were removed.

After our initial selection, we used Google Scholar to perform a forward and backward snowball search approach. We chose the snowball search method over the systematic review approach as the forward and backward snowball search methodologies offer efficient alternatives to a systematic review. Unlike systematic reviews, the snowball search method is particularly valuable when dealing with emerging fields or when the scope of inquiry is evolving, allowing researchers to quickly uncover pertinent insights and form connections between seminal and contemporary works. During each iteration of the snowball search, we applied the aforementioned criteria to include or exclude papers accordingly. We continued our snowball search procedure until it converged to the final set of papers. After repeating this over six iterations, we found no new and significant papers, suggesting we had reached a convergent set of papers.

By October 1 st (2023), our search was complete. We then organized and studied the final included publications.

3.1 Search results

Figure 2 illustrates the general flow of our search procedure, adapted from PRISMA guidelines ( Liberati et al., 2009 ). With the search keywords, we identified 1878 studies during the thematic analysis phase. We considered these studies to select the seed papers for the further snowball search process. After performing the snowball search and considering inclusion and exclusion criteria, we finally selected 154 studies including 82 studies related to creativity (comprising 60 original papers, 12 theses, and 10 review papers) and 72 studies related to design creativity (comprising 63 original papers, 5 theses, and 4 review papers). In our search, we also found 6 related textbooks and 157 studies using other modalities (such as fMRI, fNIRS, etc.) which were excluded. We used these textbooks, theses, and their resources to gain more knowledge in the initial steps of our review. Some studies using fMRI and fNIRS were used to evaluate the results in the discussion. In the snowball search process, a large number of studies have consistently appeared across all iterations implying their high relevance and influence in the field. These papers, which have been repeatedly selected throughout the search process, demonstrate their significant contributions to the understanding of design creativity and EEG studies. The snowball process effectively identifies such pivotal studies by highlighting their recurrent presence and citation in the literature, underscoring their importance in shaping the research landscape.

Figure 2 . Search procedure and results (adopted from PRISMA) using the thematic analysis in the first iteration and snowball search in the following iterations.

3.2 Design creativity neurocognition: history and trend

As discussed in Section 1, creativity and design creativity studies are different yet closely related in that design creativity involves a more complex design process. In this subsection, we will look at how the design neurocognition creativity study followed the creativity neurocognition study (though not necessarily in a causal manner).

3.2.1 History of creativity neurocognition

Three early studies in the field of creativity neurocognition are Martindale and Mines (1975) , Martindale and Hasenfus (1978) , and Martindale et al. (1984) . In the first study ( Martindale and Mines, 1975 ), it is stated that creative individuals may exhibit certain traits linked to lower cortical activation. This research has shown distinct neural activities when participants engage in two creativity tasks: the Alternate Uses Tasks (AUT) and the Remote Associate Task (RAT). The AUT, which gauges ideational fluency and involves unfocused attention, is related to higher alpha power in the brain. Conversely, the RAT, which centers on producing specific answers, shows varied alpha levels. Previous psychological research aligns with these findings, emphasizing the different nature of these tasks. Creativity, as determined by both tests, is associated with high alpha percentages during the AUT, hinting at an association between creativity and reduced cortical activation during creative tasks. However, highly creative individuals also show a mild deficit in cortical self-control, evident in their increased alpha levels, even when trying to suppress them. This behavior mirrors findings from earlier and later studies and implies that these individuals might have a predisposition to disinhibition. The varying alpha levels during cognitive tasks likely stem from their reaction to tasks rather than intentional focus shifts ( Martindale and Mines, 1975 ).

In the second study ( Martindale and Hasenfus, 1978 ), the authors explored the relationship between creativity and EEG alpha band presence during different stages of the creative process. There were two experiments in this study. Experiment 1 found that highly creative individuals had lower alpha wave presence during the elaboration stage of the creative process, while Experiment 2 found that effort to be original during the inspiration stage was associated with higher alpha wave presence. Overall, the findings suggest that creativity is associated with changes in EEG alpha wave presence during different stages of the creative process. However, the relationship is complex and may depend on factors such as effort to be original and the specific stage of the creative process.

Finally, a series of three studies indicated a link between creativity and hemispheric asymmetry during creative tasks ( Martindale et al., 1984 ). Creative individuals typically exhibited heightened right-hemisphere activity compared to the left during creative output. However, no distinct correlation was found between creativity and varying levels of hemispheric asymmetry during the inspiration versus elaboration phases. The findings suggest that this relationship is consistent across different stages of creative production. These findings were the foundation of design creativity studies which were more explored later and confirmed by other studies ( Petsche et al., 1997 ). Later studies have used these findings to validate their results. In addition to these early studies, there exist several reviews such as Fink and Benedek (2014) , Pidgeon et al. (2016) , and Rominger et al. (2022a) which provide a comprehensive literature review of previous studies and their main findings including early studies as well as recent creativity research.

3.2.2 EEG-based creativity studies

In the preceding sections, we aimed to lay a foundational understanding of neurocognition in creativity, equipping readers with essential knowledge for the subsequent content. In this subsection, we will briefly introduce the main and most important points regarding creativity experiments. More detailed information can be found in Simonton (2000) , Srinivasan (2007) , Arden et al. (2010) , Fink and Benedek (2014) , Pidgeon et al. (2016) , Lazar (2018) , and Hu and Shepley (2022) .

This section presents key details from the selected studies in a structured format to facilitate easy understanding and comparison for readers. As outlined earlier, crucial elements in creativity research include the participants, psychometric tests used, creativity tasks, EEG recording and analysis techniques, and the methods of final data analysis. We have organized these factors, along with the principal findings of each study, into Table 1 . This approach allows readers to quickly grasp the essential information and compare various aspects of different studies. The table format not only aids in presenting data clearly and concisely but also helps in highlighting similarities and differences across studies, providing a comprehensive overview of the field. Following the table, we have included a discussion section. This discussion synthesizes the information from the table, offering insights and interpretations of the trends, implications, and significance of these studies in the broader context of creativity neurocognition. This structured presentation of studies, followed by a detailed discussion, is designed to enhance the reader’s understanding, and provide a solid foundation for future research in this dynamic and evolving field.

Table 1 . A summary of EEG-based creativity neurocognition studies.

In our research, we initially conducted a thematic analysis and utilized a forward and backward snowball search method to select relevant studies. Out of these, five studies employed machine learning techniques, while the remaining ones concentrated on statistically analyzing EEG features. It is noteworthy that all the chosen studies followed a similar methodology, involving the recruitment of participants, administering probable psychometric tests, conducting creativity tasks, recording EEG data, and concluding with final data analysis.

While most studies follow similar structure for their experiments, some other studies focus on other aspects of creativity such as artistic creativity and poetry, targeting different evaluation methods, and through different approaches. In Shemyakina and Dan’ko (2004) and Danko et al. (2009) , the authors targeted creativity to produce proverbs or definitions of emotions of notions. In other studies ( Leikin, 2013 ; Hetzroni et al., 2019 ), the experiments are focused on creativity and problem-solving in autism and bilingualism. Moreover, some studies such as Volf and Razumnikova (1999) and Razumnikova (2004) focus more on the gender differences in brain organization during creativity tasks. In another study ( Petsche, 1996 ), approaches to verbal, visual, and musical creativity were explored through EEG coherence analysis. Similarly, the study ( Bhattacharya and Petsche, 2005 ) analyzed brain dynamics in mentally composing drawings through differences in cortical integration patterns between artists and non-artists. We summarized the findings of EEG-based creativity studies in Table 1 .

3.2.3 Neurocognitive studies of design and design creativity

Design is closely associated with creativity. On the one hand, by definition, creativity is a measure of the process of creating, for which design, either intentional or unconscious, is an indispensable constituent. On the other hand, it is important to note that not all designs are inherently creative; many designs follow established patterns and resemble existing ones, differing only in their specific context. Early research on design creativity aimed to differentiate between design and design creativity tasks by examining when and how designers exhibited creativity in their work. In recent years, much of the focus in design creativity research has shifted towards cognitive and neurocognitive investigations, as well as the development of computational models to simulate creative processes ( Borgianni and Maccioni, 2020 ; Lloyd-Cox et al., 2022 ). Neurocognitive studies employ neuroimaging methods (such as EEG) while computational models often leverage artificial intelligence or cognitive modeling techniques ( Zeng and Yao, 2009 ; Gero, 2020 ; Gero and Milovanovic, 2020 ). In this section, we review significant EEG-based studies in design creativity to focus more on design creation and highlight the differences. While most studies have processed EEG to provide more detailed insight into brain dynamics, some studies such as Goel (2014) outlined a preliminary framework encompassing cognitive and neuropsychological systems essential for explaining creativity in designing artifacts.

Several studies have recorded and analyzed EEG in design and design creativity tasks. Most neuro-cognitive studies have directly or indirectly employed frequency-based analysis which is based on the analysis of EEG in specific frequency bands including delta (0.5–4 Hz), theta (4–8 Hz), alpha (8–13 Hz), beta (13–30 Hz), and gamma (>30 Hz). One of the main analyses is called task-related potential (TRP) which has provided a foundation for other analyses. It computes the relative power of the EEG signal associated with a design task in a specific frequency band with respect to the power of EEG in the rest mode. This analysis is simple and effective and reveals the physiological processes underlying EEG dynamics ( Rominger et al., 2018 ; Jia and Zeng, 2021 ; Gubler et al., 2022 ; Rominger et al., 2022b ).

Frequency-based analyses have been widely employed. For example, the study ( Borgianni and Maccioni, 2020 ) applied TRP analysis to compare the neurophysiological activations of mechanical engineers and industrial designers while conducting design tasks including problem-solving, basic design, and open design. These studies have agreed that higher alpha band activity is sensitive to specific task-related requirements, while the lower alpha corresponds to attention processes such as vigilance and alertness ( Klimesch et al., 1998 ; Klimesch, 1999 ; Chrysikou and Gero, 2020 ). Higher alpha activity in the prefrontal region reflects complex cognitive processes, higher internal attention (such as in imagination), and task-irrelevant inhibition ( Fink et al., 2009a , b ; Fink and Benedek, 2014 ). On the other hand, higher alpha activity in the occipital and temporal lobes corresponds to visualization processes ( Vieira et al., 2022a ). In design research, to compare EEG characteristics in design activities (such as idea generation or evaluation) ( Liu et al., 2016 ), frequency-based analysis has been widely employed ( Liu et al., 2018 ). Higher alpha is associated with open-ended tasks, visual association in expert designers, and divergent thinking ( Nguyen and Zeng, 2014b ; Nguyen et al., 2019 ). Higher beta and theta play a pivotal role in convergent thinking, and constraint tasks ( Nguyen and Zeng, 2010 ; Liu et al., 2016 ; Liang and Liu, 2019 ).

The research in design and design creativity is not limited to frequency-based analyses. Nguyen et al. (2019) introduced Microstate analysis to EEG-based design studies. Using the microstate analysis, Jia and Zeng investigated EEG characteristics in design creativity experiment ( Jia and Zeng, 2021 ), where EEG signals were recorded while participants conducted design creativity experiments which were modified TTCT tasks ( Nguyen and Zeng, 2014b ).

Following the same approach, Jia et al. (2021) analyzed EEG microstates to decode brain dynamics in design cognitive states including problem understanding, idea generation, rating idea generation, idea evaluation, and rating idea evaluation, where six design problems including designing a birthday cake, a toothbrush, a recycle bin, a drinking fountain, a workplace, and a wheelchair were used for the EEG based design experimental studies ( Nguyen and Zeng, 2017 ). The data of these two loosely controlled EEG-based design experiments are summarized and available for the research community ( Zangeneh Soroush et al., 2024 ).

We summarized the findings of EEG-based design and design creativity studies in Table 2 .

Table 2 . A summary of EEG-based design creativity neurocognition studies.

3.2.4 Trend analysis

The selected studies span a broad range of years, stretching from 1975 ( Martindale and Mines, 1975 ) to the present day, reflecting advancements in neuro-imaging techniques and machine learning methods that have significantly aided researchers in their investigations. From the earliest studies to more recent ones, the primary focus has centered on EEG sub-bands, brain asymmetry, coherence analysis, and brain topography. Recently, machine learning methods have been employed to classify EEG samples into designers’ cognitive states. These studies can be roughly classified into the following distinct categories based on their proposed experiments and EEG analysis methods ( Pidgeon et al., 2016 ; Jia, 2021 ): (1) visual creativity versus baseline rest/fixation, (2) visual creativity versus non-rest control task(s), (3) individuals of high versus low creativity, (4) generation of original versus standard visual images, (5) creativity in virtual reality vs. real environment, (6) loosely controlled vs. strictly controlled creativity experiments.

The included studies exhibited considerable variation in the tasks utilized and the primary contrasts examined. Some studies employed frequency-based or EEG power analysis to compare brain activity during visual creativity tasks with tasks involving verbal creativity or both verbal and visual tasks. These tasks often entail memory tasks or tasks focused on convergent thinking. Several studies, however, adopted a simpler approach by comparing electrophysiological activity during visual creativity tasks against a baseline fixation or rest condition. Some studies compared neural activities between individuals characterized by high and low levels of creativity, while others compared the generation of original creative images with that of standard creative images. Several studies analyze brain behavior concerning creativity factors such as fluency, originality, and others. These studies typically employ statistical analysis techniques to illustrate and elucidate differences between various creativity factors and their corresponding brain behaviors. This variability underscores the diverse approaches taken by researchers to examine the neural correlates of design creativity ( Pidgeon et al., 2016 ). However, few studies significantly and deeply delved into areas such as gender differences in creativity, creativity among individuals with mental or physical disorders, or creativity in diverse job positions or skill sets. This suggests that there is significant untapped potential within the EEG-based design creativity research area.

In recent years, advancements in fMRI imaging and its applications have led several studies to replace EEG with fMRI to investigate brain behavior. fMRI extracts metabolism, resulting in relatively high spatial resolution compared to EEG. However, it is important to note that fMRI has lower temporal resolution compared to EEG. Despite this difference, the shift towards fMRI highlights the ongoing evolution and exploration of neuroimaging techniques in understanding the neural correlates of design creativity. fMRI studies provide a deep understanding of neural circuits associated with creativity and can be used to evaluate EEG-based studies ( Abraham et al., 2018 ; Japardi et al., 2018 ; Zhuang et al., 2021 ).

The emergence of virtual reality (VR) has had a significant impact on design creativity studies, offering a wide range of experimentation possibilities. VR enables researchers to create diverse scenarios and creativity tasks, providing a dynamic and immersive environment for participants ( Agnoli et al., 2021 ; Chang et al., 2022 ). Through VR technology, various design creativity experiments can be conducted, allowing for novel approaches and innovative methodologies to explore the creative process. This advancement opens up new avenues for researchers to investigate the complexities of design creativity more interactively and engagingly.

Regardless of the significant progress over the past few decades, design and design creativity neurocognitive research is still in its early stages, due to the challenges identified ( Zhao et al., 2020 ; Jia et al., 2021 ), which is summarized below:

1. Design tasks are open-ended, meaning there is no single correct outcome and countless acceptable solutions are possible. There are no predetermined or optimal design solutions; multiple feasible solutions may exist for an open-ended design task.

2. Design tasks are ill-defined as finding a solution might change or redefine the original task, leading to new tasks emerging.

3. Various emergent design tasks trigger design knowledge and solutions, which in turn can change or redefine tasks further.

4. The process of completing a design task depends on emerging tasks and the perceived priorities for completion.

5. The criteria to evaluate a design solution are set by the solution itself.

While a lot of lessons learned from creativity neurocognitive research can be borrowed to study design and design creativity neurocognition, new paradigms should be proposed, tested, and validated to advance this new discipline. This advancement will in turn move forward creativity neurocognition research.

3.3 Experiment protocol

Concerning the model described in Figure 1 , we arranged the following sections to cover all the main components of EEG-based design creativity studies. To bring a general picture of the EEG-based design creativity studies, we briefly explain the procedure of such experiments. Since most design creativity neurocognition research inherited more or less procedures in general creativity research, we will focus on AUT and TTCT tasks. The introduction of a loosely controlled paradigm, tEEG, can be found in Zhao et al. (2020) , Jia et al. (2021) , and Jia and Zeng (2021) . Taking a look at Tables 1 , 2 , it can be inferred that almost all included studies record EEG signals while selected participants are performing creativity tasks. The first step is determining the sample size, recruiting participants, and psychometrics according to which participants get selected. In some of these studies, participants take psychometric tests before performing the creativity tasks for screening or categorization. In this review, the tasks used to gauge creativity are the Alternative Uses Test (AUT) and the Torrance Test of Creative Thinking (TTCT). During these tasks, EEG is recorded and then preprocessed to remove any probable artifacts. These artifact-free EEGs are then processed to extract specific features, which are subsequently subjected to either statistical analysis or machine learning methods. Statistical analysis typically compares brain dynamics across different creativity tasks like idea generation, idea evolution, and idea evaluation. Machine learning, on the other hand, categorizes EEG signals based on associated creativity tasks. The final stage involves data analysis, which aims to deduce how brain dynamics correlate with the creativity tasks given to participants. This data analysis also compares EEG results with psychometric test findings to discern any significant differences in EEG dynamics and neural activity between groups.

3.3.1 Participants

The first factor of the studies is their participants. In most studies, participants are right-handed, non-medicated, and have normal or corrected to normal vision. In some cases, the Edinburgh Handedness Inventory ( Oldfield, 1971 ) (with 11 elements) or hand dominance test (HDT) ( Steingrüber et al., 1971 ) were employed to determine participants’ handedness ( Rominger et al., 2020 ; Gubler et al., 2023 ; Mazza et al., 2023 ). While in several creativity studies, right-handedness has been considered; relatively, in design creativity studies it has been less mentioned.

In most studies, participants are undergraduate or graduate students with different educational backgrounds and an age range of 18 to 30 years. In the included papers, participants did not report any history of psychiatric or neurological disorders, or treatment. It should be noted that some studies such as Ayoobi et al. (2022) and Gubler et al. (2022) analyzed creativity in health conditions like multiple sclerosis or participants with chronic pain, respectively. These studies usually conduct statistical analysis to investigate the results of creativity tasks such as AUT or Remote Association Task (RAT) and then associate the results with the health condition. In some studies, it is reported that participants were asked not to smoke cigarettes for 1 h, not to have coffee for 2 h, alcohol for 12 h, or other stimulating beverages for several hours before experiments. As mentioned in some design creativity studies, similar rules apply to design creativity experiments (participants are not allowed to have stimulating beverages).

In most studies, the sample size of participants was as large as 15 up to 45 participants except for a few studies ( Jauk et al., 2012 ; Perchtold-Stefan et al., 2020 ; Rominger et al., 2022a , b ) which had larger numbers such as 100, 55, 93, and 74 participants, respectively. Some studies such as Agnoli et al. (2020) and Rominger et al. (2020) calculated their required sample size through G*power software ( Faul et al., 2007 ) concerning their desirable chance (or power) of detecting a specific interaction effect involving the response, hemisphere, and position ( Agnoli et al., 2020 ). Considering design creativity studies, the same trend can be seen as the minimum and maximum numbers of participants are 8 and 84, respectively. Similarly, in a few studies, sample sizes were estimated through statistical methods such as G*power ( Giannopulu et al., 2022 ).

In most studies, a considerable number of participants were excluded due to several reasons such as not being fluent in the language used in the experiment, left-handedness, poor quality of recorded signals, extensive EEG artifacts, misunderstanding the procedure of the experiment correctly, technical errors, losing the data during the experiment, no variance in the ratings, and insufficient behavioral data. This shows that recording a high-quality dataset is quite challenging as several factors determine whether the quality is acceptable. Two datasets (in design and creativity) with public access have recently been published in Mendeley Data ( Zangeneh Soroush et al., 2023a , b ). Except for these two datasets, to the best of our knowledge, there is no publicly available dataset of EEG signals recorded in design and design creativity experiments.

Regarding the gender analysis, among the included papers, there were a few studies which directly focused on the association between gender, design creativity, and brain dynamics ( Vieira et al., 2021 , 2022a ). In addition, most of the included papers did not choose the participants’ gender to include or exclude them. In some cases, participants’ genders were not reported.

3.3.2 Psychometric tests

Before the EEG recording sessions, participants are often screened using psychometric tests, which are usually employed to categorize participants based on different aspects of intellectual abilities, ideational fluency, and cognitive development. These tests provide scores on various cognitive abilities. Additionally, personality tests are used to create personas for participants. Self-report questionnaires measure traits such as anxiety, mood, and depression. Some of the psychometric tests include the Intelligenz-Struktur-Test 2000-R (I-S-T 2000 R), which assesses general mental ability and specific intellectual abilities like visuospatial, numerical, and verbal abilities. The big five test is used for measuring personality traits like conscientiousness, extraversion, neuroticism, openness to experience, and agreeableness. Other tests such as Spielberger’s state–trait anxiety inventory (STAI) are used for mood and anxiety, while the Eysenck Personality Questionnaire (EPQ-R) investigates possible personality correlates of task performance ( Fink and Neubauer, 2006 , 2008 ; Fink et al., 2009a ; Jauk et al., 2012 ; Wang et al., 2019 ). To the best of our knowledge, the included design creativity studies have not directly utilized psychometrics ( Table 2 ) to explore the association between participants’ cognitive characteristics and brain behavior. There exist a few studies which have indirectly used cognitive characteristics. For instance, Eymann et al. (2022) assessed the shared mechanisms of creativity and intelligence in creative reasoning and their correlations with EEG characteristics.

3.3.3 Creativity and design creativity tasks

In this section, we introduce some experimental creativity tasks such as the Alternate Uses Task (AUT), and the Torrance Test of Creative Thinking (TTCT). Here, we would like to shed light on these tasks and their correlation with design creativity. One of the main characteristics of design creativity is divergent thinking as its first phase which is addressed by these two creativity tasks. In addition, AUT and TTCT are adopted and modified by several studies such as Hartog et al. (2020) , Hartog (2021) , Jia et al. (2021) , Jia and Zeng (2021) , and Li et al. (2021) for design creativity neurocognition studies. The figural version of TTCT is aligned with the goals of design creativity tasks where designers (specifically in engineering domains) create or draw their ideas, generate solutions, and evaluate and evolve generated solutions ( Srinivasan, 2007 ; Mayseless et al., 2014 ; Jia et al., 2021 ).

Furthermore, design creativity studies have introduced different types of design tasks from sequence of simple design problems to constrained and open design tasks ( Nguyen et al., 2018 ; Vieira et al., 2022a ). This variety of tasks opens new perspectives to the design creativity neurocognition studies. For example, the six design problems have been employed in some studies ( Nguyen and Zeng, 2014b ); ill-defined design tasks are used to explore brain dynamics differences between novice and expert designers ( Vieira et al., 2020d ).

The Alternate Uses Task (AUT), established by Guilford (1967) , is a prominent tool in psychological evaluations for assessing divergent thinking, an essential element of creativity. In AUT ( Guilford, 1967 ), participants are prompted to think of new and unconventional uses for everyday objects. Each object is usually shown twice – initially in the normal (common) condition and subsequently in the uncommon condition. In the common condition, participants are asked to consider regular, everyday uses for the objects. Conversely, in uncommon conditions, they are encouraged to come up with unique, inventive uses for the objects ( Stevens and Zabelina, 2020 ). The test includes several items for consideration, e.g., brick, foil, hanger, helmet, key, magnet, pencil, and pipe. In the uncommon condition, participants are asked to come up with as many uses as they can for everyday objects, such as shoes. It requires them to think beyond the typical uses (e.g., foot protection) and envision novel uses (e.g., a plant pot or ashtray). The responses in this classic task do not distinguish between the two key elements of creativity: originality (being novel and unique) and appropriateness (being relevant and meaningful) ( Runco and Mraz, 1992 ; Wang et al., 2017 ). For instance, when using a newspaper in the AUT, responses can vary from common uses like reading or wrapping to more inventive ones like creating a temporary umbrella. The AUT requires participants to generate multiple uses for everyday objects thereby measuring creativity through four main criteria: fluency (quantity of ideas), originality (uniqueness of ideas), flexibility (diversity of idea categories), and elaboration (detail in ideas) ( Cropley, 2000 ; Runco and Acar, 2012 ). In addition to the original indices of AUT, there are some creativity tests which include other indices such as fluency-valid and usefulness. Usefulness refers to how functional the ideas are ( Cropley, 2000 ; Runco and Acar, 2012 ) whereas fluency-valid, which only counts unique and non-repeated ideas, is defined as a valid number of ideas ( Prent and Smit, 2020 ). The AUT’s straightforward design and versatility make it a favored method for gauging creative capacity in diverse groups and settings, reflecting its universal applicability in creativity assessment ( Runco and Acar, 2012 ).

Developed by E. Paul Torrance in the late 1960s, the Torrance Test of Creative Thinking (TTCT) ( Torrance, 1966 ) is a foundational instrument for evaluating creative thinking. TTCT is recognized as a highly popular and extensively utilized tool for assessing creativity. Unlike the AUT, the TTCT is more structured and exists in two versions: verbal and figural. The verbal part of the TTCT, known as TTCT-Verbal, includes several subtests ( Almeida et al., 2008 ): (a) Asking Questions and Making Guesses (subtests 1, 2, and 3), where participants are required to pose questions and hypothesize about potential causes and effects; (b) Improvement of a Product (subtest 4), which involves suggesting modifications to the product; (c) Unusual Uses (subtest 5), where participants think of creative and atypical uses; and (d) Supposing (subtest 6), where participants imagine the outcomes of an unlikely event, as per Torrance. The figural component, TTCT-Figural, contains three tasks ( Almeida et al., 2008 ): (a) creating a drawing; (b) completing an unfinished drawing; and (c) developing a new drawing starting from parallel lines. An example of a figural TTCT task might involve uniquely finishing a partially drawn image, with evaluations based on the aforementioned criteria ( Rominger et al., 2018 ).

The TTCT includes a range of real-world reflective activities that encourage diverse thinking styles, essential for daily life and professional tasks. The TTCT assesses abilities in Questioning, Hypothesizing Causes and Effects, and Product Enhancement, each offering insights into an individual’s universal creative potential and originality ( Boden, 2004 ; Runco and Jaeger, 2012 ; Sternberg, 2020 ). It acts like a comprehensive test battery, evaluating multiple facets of creativity’s complex nature ( Guzik et al., 2023 ).

There are also other creativity tests such as Remote Associates Test (RAT), Runco Creativity Assessment Battery (rCAB), and Consensual Assessment Technique (CAT). TTCT is valued for its extensive historical database of human responses, which serves as a benchmark for comparison, owing to the consistent demographic profile of participants over many years and the systematic gathering of responses for evaluation ( Kaufman et al., 2008 ). The Alternate Uses Task (AUT) and the Remote Associates Test (RAT) are appreciated for their straightforward administration, scoring, and analysis. The Creative Achievement Test (CAT) is notable for its adaptability to specific fields, made possible by employing a panel of experts in relevant domains to assess creative works. Consequently, the CAT is particularly suited for evaluating creative outputs in historical contexts or significant “Big-C” creativity ( Kaufman et al., 2010 ). In contrast, the AUT and TTCT are more relevant for examining creativity in everyday, psychological, and professional contexts. As such, AUT and TTCT tests will establish a solid baseline for more complex design creativity studies employing more realistic design problems.

3.4 EEG recording and analysis: methods and algorithms

Electroencephalogram (EEG) signal analysis is a crucial component in the study of creativity whereby brain behavior associated with creativity tasks can be explored. Due to its advantages, EEG has emerged as one of the most suitable neuroimaging techniques for investigating brain activity during creativity tasks. Its affordability and suitability for studies involving physical movement, ease of recording and usage, and notably high temporal resolution make EEG a preferred choice in creativity research.

The dynamics during creative tasks are complex, nonlinear, and self-organized ( Nguyen and Zeng, 2012 ). It can thus be assumed that the brain could exhibits the similar characteristics, which shall be reflected in EEG signals. Capturing these complex and nonlinear patterns of brain behavior can be challenging for other neuroimaging methods ( Soroush et al., 2018 ).

3.4.1 Preprocessing: artifact removal

In design creativity studies utilizing EEG, the susceptibility of EEG signals to noise and artifacts is a significant concern due to the accompanying physical movements inherent in these tasks. Consequently, EEG preprocessing becomes indispensable in ensuring data quality and reliability. Unfortunately, not all the included studies in this review have clearly explained their pre-processing and artifact removal approaches. There also exist some well-known preprocessing pipelines such as HAPPE ( Gabard-Durnam et al., 2018 ) which (in contrast to their high efficiency) have been rarely used in design creativity neurocognition ( Jia et al., 2021 ; Jia and Zeng, 2021 ). The included papers in our analysis have introduced various preprocessing methods, including wavelet analysis, frequency-based filtering, and independent component analysis (ICA) ( Beaty et al., 2017 ; Fink et al., 2018 ; Lou et al., 2020 ). The primary objective of preprocessing remains consistent: to obtain high-quality EEG data devoid of noise or artifacts while minimizing information loss. Achieving this goal is crucial for the accurate interpretation and analysis of EEG signals in design creativity research.

3.4.2 Preprocessing: segmentation

Design creativity studies often encompass a multitude of cognitive tasks occurring simultaneously or sequentially, rendering them ill-defined and unstructured. This complexity leads to the generation of unstructured EEG data, posing a challenge for subsequent analysis ( Zhao et al., 2020 ). Therefore, segmentation methods play a crucial role in classifying recorded EEG signals into distinct cognitive tasks, such as idea generation, idea evolution, and idea evaluation.

Several segmentation methods have been adopted, including the ones relying on Task-Related Potential (TRP) analysis and microstate analysis, followed by clustering techniques like K-means clustering ( Nguyen and Zeng, 2014a ; Nguyen et al., 2019 ; Zhao et al., 2020 ; Jia et al., 2021 ; Jia and Zeng, 2021 ; Rominger et al., 2022b ). These methods aid in organizing EEG data into meaningful segments corresponding to different phases of the design creativity process, facilitating more targeted and insightful analysis. In addition, they provide possibilities to look into a more comprehensive list of design-related cognitions implied in but not intended by conventional AUT and TTCT experiments.

While there are some uniform segmentation methods (such as the ones based on TRP) employing frequency-based methods. Nguyen et al. (2019) introduced a fully automatic dynamic method based on microstate analysis. Since then, microstate analysis has been used in several studies to categorize the EEG dynamics in design creativity tasks ( Jia et al., 2021 ; Jia and Zeng, 2021 ). Microstate analysis provides a novel method for EEG-based design creativity studies with the capabilities of high temporal resolution and topography results ( Yuan et al., 2012 ; Custo et al., 2017 ; Jia et al., 2021 ; Jia and Zeng, 2021 ).

3.4.3 Feature extraction

The EEG data, after undergoing preprocessing, is directed to feature extraction, where relevant attributes are extracted to delve deeper into EEG dynamics and brain activity. These extracted features serve as the basis for conducting statistical analyses or employing machine learning algorithms.

In our review of the literature, we found that EEG frequency, time, and time-frequency analyses are the most commonly employed methods among the papers we considered. Specifically, the EEG alpha, beta, and gamma bands are often highlighted as critical indicators for studying brain dynamics in creativity and design creativity. Significant variations in the EEG bands have been observed during different stages of design creation tasks, including idea generation, idea evaluation, and idea elaboration ( Nguyen and Zeng, 2010 ; Liu et al., 2016 ; Rominger et al., 2019 ; Giannopulu et al., 2022 ; Lukačević et al., 2023 ; Mazza et al., 2023 ). For instance, the very first creativity studies used EEG alpha asymmetry to explore the relationship between creativity and left-hemisphere and right-hemisphere brain activity ( Martindale and Mines, 1975 ; Martindale and Hasenfus, 1978 ; Martindale et al., 1984 ). Other studies divided the EEG alpha band into lower (8–10 Hz) and upper alpha (10–13 Hz) and concluded that low alpha is more significant compared to the high EEG alpha band. Although the alpha band has been extensively explored by previous studies, several studies have also analyzed other EEG sub-bands such as beta, gamma, and delta and later concluded that these sub-bands are also significantly associated with creativity mechanisms, and can explain the differences between genders in different creativity experiments ( Razumnikova, 2004 ; Volf et al., 2010 ; Nair et al., 2020 ; Vieira et al., 2022a ).

Several studies have utilized Task-related power changes (TRP) to compare the EEG dynamics in different creativity tasks. TRP analysis is a high-temporal resolution method used to examine changes in brain activity associated with specific tasks or cognitive processes. In TRP analysis, the power of EEG signals, typically measured in terms of frequency bands (like alpha, beta, theta, etc.), is analyzed to identify how brain activity varies during the performance of a task compared to baseline or resting states. This method is particularly useful for understanding the dynamics of brain function as it allows researchers to pinpoint which areas of the brain are more active or less active during specific cognitive or motor tasks ( Rominger et al., 2022b ; Gubler et al., 2023 ). Reportedly, TRP has wide usage in EEG-based design creativity studies ( Jia et al., 2021 ; Jia and Zeng, 2021 ; Gubler et al., 2022 ).

Event-related synchronization (ERS) and de-synchronization (ERD) have also been reported to be effective in creativity studies ( Wang et al., 2017 ). ERD refers to a decrease in EEG power (in a specific frequency band) compared to a baseline state. The reduction in alpha power, for instance, is often interpreted as an increase in cortical activity. Conversely, ERS denotes an increase in EEG power. The increase in alpha power, for example, is associated with a relative decrease in cortical activity ( Doppelmayr et al., 2002 ; Babiloni et al., 2014 ). Researchers have concluded that these two indicators play a pivotal role in creativity studies as they are significantly correlated with brain dynamics during creativity tasks ( Srinivasan, 2007 ; Babiloni et al., 2014 ; Fink and Benedek, 2014 ).

Brain functional connectivity analysis, EEG source localization, brain topography maps, and event-related potentials analysis are other EEG processing methods which have been employed in a few studies ( Srinivasan, 2007 ; Dietrich and Kanso, 2010 ; Giannopulu et al., 2022 ; Kuznetsov et al., 2023 ). Considering that these methods have not been employed in several studies and with respect to their potential to provide insight into brain activity in transient modes or the correlations between the brain lobes, future studies are suggested to utilize such methods.

3.4.4 Data analysis and knowledge extraction

What was mentioned indicates that EEG frequency analysis is an effective approach for examining brain behavior in creativity and design creativity processes ( Fink and Neubauer, 2006 ; Nguyen and Zeng, 2010 ; Benedek et al., 2011 , 2014 ; Wang et al., 2017 ; Rominger et al., 2018 ; Vieira et al., 2022b ). Analyzing EEG channels in the time or frequency domains across various creativity tasks helps identify key channels contributing to these experiments. TRP and ERD/ERS are well-known EEG analysis methods widely applied in the included studies. Some studies have used other EEG sub-bands such as delta or gamma ( Boot et al., 2017 ; Stevens and Zabelina, 2020 ; Mazza et al., 2023 ). Besides these methods, other studies have utilized EEG connectivity and produced brain topography maps to explore different stages of design creativity. The final stage of EEG-based research involves statistical analysis and classification.

In statistical analysis, researchers examine EEG characteristics like power or alpha band amplitude to determine if there are notable differences during creativity tasks. Comparisons are made across different brain lobes and participants to identify which brain regions are more active during various stages of creativity. Techniques such as TRP, ERD, and ERS are scrutinized using statistical hypothesis testing to see if brain dynamics vary among participants or across different creativity tasks. Additionally, the relationship between EEG features and creativity scores is explored. For instance, researchers might investigate whether there is a link between EEG alpha power and creativity scores like originality and fluency. These statistical analyses can be conducted through either temporal or frequency EEG data.

In the classification phase, EEG data are classified according to different cognitive states of the brain. For example, EEG recordings might be classified based on the stages of creativity tasks, such as idea generation and idea evolution ( Hu et al., 2017 ; Stevens and Zabelina, 2020 ; Lloyd-Cox et al., 2022 ; Ahad et al., 2023 ; Şekerci et al., 2024 ). Except for a few studies which employed machine learning, other studies targeted EEG analysis and statistical methods. In these studies, the main objective is reported to be the classification of designers’ cognitive states, their emotional states, or the level of their creativity. In the included papers, traditional classifiers such as support vector machines and k-nearest neighbor have been employed. Modern deep learning approaches can be used in future studies to extract the hidden valuable information of EEG in design creativity states ( Jia, 2021 ). In open-ended loosely controlled creativity studies, where the phases of creativity are not clearly defined, clustering techniques are employed to categorize or segment EEG time intervals according to the corresponding creativity tasks ( Jia et al., 2021 ; Jia and Zeng, 2021 ). While loosely controlled design creativity studies results in more reliable and natural outcomes compared to strictly controlled ones, analyzing EEG signals in loosely controlled experiments is challenging as the recorded signals are not structured. Clustering methods are applied to microstate analysis to segment EEG signals into pre-defined states and have structured blocks that may align with certain cognitive functions ( Nguyen et al., 2019 ; Jia et al., 2021 ; Jia and Zeng, 2021 ). Therefore, statistical analysis, classification, and clustering form the core methods of data analysis in studies of creativity.

Table 2 represents EEG-based design studies with details about the number of participants, probable psychometric tests, experiment protocol, EEG analysis methods, and main findings. These studies are reported in this paper to highlight some of the differences between creativity and design creativity.

In addition to the studies reported in Table 2 , previous reviews and studies ( Srinivasan, 2007 ; Nguyen and Zeng, 2010 ; Lazar, 2018 ; Chrysikou and Gero, 2020 ; Hu and Shepley, 2022 ; Kim et al., 2022 ; Balters et al., 2023 ) can be found, which comprehensively reported approaches in design creativity neurocognition. Moreover, neurophysiological studies in design creativity are not limited to EEG or the components in Table 2 . For instance, in Liu et al. (2014) , EEG, heart rate (HR), and galvanic skin response (GSR) was used to detect the designer’s emotions in computer-aided design tasks. They determined the emotional states of CAD design tasks by processing CAD operators’ physiological signals and a fuzzy logic model. Aiello (2022) investigated the effects of external factors (such as light) and human ones on design processes, which also explored the association between the behavioral and neurophysiological responses in design creativity experiments. They employed ANOVA tests and found a significant correlation between neurophysiological recordings and daytime, participants’ stress, and their performance in terms of novelty and quality. They also recognized different patterns of brain dynamics corresponding to different kinds of performance measures. Montagna et al. ( Montagna and Candusso, n.d. ; Montagna and Laspia, 2018 ) analyzed brain behavior during the creative ideation process in the earliest phases of product development. In addition to EEG, they employed eye tracking to analyze the correlations between brain responses and eye movements. They utilized statistical analysis to recognize significant differences in brain hemispheres and lobes with respect to participants’ background, academic degree, and gender during the two modes of divergent and convergent thinking. Although some of their results are not consistent with those from the literature, these experiments shed light on the experiment design and provide insights and a framework for future experiments.

4 Discussion

In the present paper, we reviewed EEG-based design creativity studies in terms of their main components such as participants, psychometrics, and creativity tasks. Numerous studies have delved into brain activities associated with design creativity tasks, examined from various angles. While Table 1 showcases studies centered on the Alternate Uses Test (AUT), and the Torrance Tests of Creative Thinking (TTCT), Table 2 summarizes the EEG-based studies on design and design creativity-related tasks. In this section, we are going to discuss the impact of some most important factors including participants, experiment design, and EEG recording and processing on EEG-based design creativity studies. Research gaps and open questions are thus presented based on the discussion.

4.1 Participants

4.1.1 psychometrics: do we have a population that we wished for.

Psychometric testing is crucial for participant selection, with participant screening often based merely on self-reported information or based on their educational background. Examining Tables 1 , 2 reveals that psychometrics are not frequently utilized in design creativity studies, indicating a notable gap in these investigations. Future research should consider establishing a standard set of psychometric tests to create comprehensive participant profiles, particularly focusing on intellectual capabilities ( Jauk et al., 2015 ; Ueno et al., 2015 ; Razumnikova, 2022 ). Taking a look at the studies which employed psychometrics, it could be inferred that there is a correlation between cognitive abilities such as intelligence and creativity ( Arden et al., 2010 ; Jung and Haier, 2013 ). The few psychometric tests employed primarily focus on determining and providing a cognitive profile, encompassing factors such as mood, stress, IQ, anxiety, memory, and intelligence. Notably, intelligence-related assessments are more commonly used compared to other tests. These psychometrics are subject to social masking according to which there is the possibility of unreliable self-report psychometrics being recorded in the experiments. These results might yield less accurate findings.

4.1.2 Sample size and participants’ characteristics

Participant numbers in these studies vary widely, indicating a broad spectrum of sample sizes in this research area. The populations in the studies varied in size, with most having around 40 participants, predominantly students. In the design of experiments, it is important to highlight that the sample size in the selected studies had a mean of 43.76 and a standard deviation of 20.50. It is worth noting that while some studies employed specific experimental designs to determine sample size, many did not have clear and specific criteria for sample size determination, leaving the ideal sample size in such studies an open question. Any studies determine their sample sizes using G* power ( Erdfelder et al., 1996 ; Faul et al., 2007 ), a prevalent tool for power analysis in social and behavioral research.

Initial investigations typically involved healthy adults to more thoroughly understand creativity’s underlying mechanisms. These foundational studies, conducted under optimal conditions, aimed to capture the essence of brain behavior during creative tasks. A handful of studies ( Ayoobi et al., 2022 ; Gubler et al., 2022 , 2023 ) have begun exploring creativity in the context of chronic pain or multiple sclerosis, but broader participant diversity remains an area for further research. Additionally, not all studies provided information on the ages of their participants. There is a noticeable gap in research involving older adults or those with health conditions, suggesting an area ripe for future exploration. Diversity in participant backgrounds, such as varying academic disciplines, could offer richer insights, given creativity’s multifaceted nature and its link to individual skills, affect, and perceived workload ( Yang et al., 2022 ). For instance, the creative approaches of students with engineering thinking might differ significantly from those with art thinking.

Gender was not examined in most included studies. There are just a few studies analyzing the effects of gender on creativity and design creativity ( Razumnikova, 2004 ; Volf et al., 2010 ; Vieira et al., 2020b , 2022a ; Gubler et al., 2022 ). There is a notable need for further investigation to fully understand the impact of gender on the brain dynamics of design creativity.

4.2 Experiment design

While the Alternate Uses Test (AUT) and the Torrance Tests of Creative Thinking (TTCT) are commonly used in creativity research, other tasks like the Remote Associate Task are also prevalent ( Schuler et al., 2019 ; Zhang et al., 2020 ). AUT and figural TTCT are particularly favored in design creativity experiments for their compatibility with design tasks, surpassing verbal or other creativity tasks in applicability ( Boot et al., 2017 ). When considering the creativity tasks in the studies, it is notable that the AUT is more frequently utilized than TTCT, owing to its simplicity and ease of quantifying creativity scores. In contrast, TTCT often requires subjective assessments and expert ratings for scoring ( Rogers et al., 2023 ). However, both TTCT and AUT have undergone modifications in several studies to investigate their potential characteristics further ( Nguyen and Zeng, 2014a ).

While the majority of studies have adhered to strictly controlled frameworks for their experiments, two studies ( Nguyen and Zeng, 2017 ; Nguyen et al., 2019 ; Jia, 2021 ; Jia et al., 2021 ) have adopted novel, loosely controlled approaches, which reportedly yield more natural and reliable results compared to the strictly controlled ones. The rigidity from strictly controlled creativity experiments can exert additional cognitive stress on participants, potentially impacting experimental outcomes. In contrast, the loosely controlled experiments are characterized as self-paced and open-ended, allowing participants ample time to comprehend the design problem, generate ideas, evaluate them, and iterate upon them as needed. Recent behavioral and theoretical research suggests that creativity is better explored within a loosely controlled framework, where sufficient flexibility and freedom are essential. This approach, which contrasts with the highly regulated nature of traditional creativity studies, aims to capture the unpredictable elements of design activities ( Zhao et al., 2020 ). Loosely controlled design studies offer a more realistic portrayal of the actual design process. In these settings, participants enjoy the liberty to develop ideas at their own pace, reflecting true design practices ( Jia, 2021 ). The flexibility in such experiments allows for a broader range of scenarios and outcomes, depending on the complexity and the designers’ understanding of the tests and processes. Prior research has confirmed the effectiveness of this approach, examining its validity from both neuropsychological and design perspectives. Despite their less rigid structure, these loosely controlled experiments are valid and consistent with previous studies. Loosely controlled creativity experiments allow researchers to engage with the nonlinear, ill-defined, open-ended, and intricate nature of creativity tasks. However, it is important to note that data collection and processing can pose challenges in loosely controlled experiments due to the resulting unstructured data. These challenges can be handled through machine learning and signal processing methods ( Zhao et al., 2020 ). For further details regarding the loosely controlled experiments, readers can refer to the provided references ( Zhao et al., 2020 ; Jia et al., 2021 ; Jia and Zeng, 2021 ; Zangeneh Soroush et al., 2024 ).

Participants are affected by external or internal sources during the experiments. Participants are asked not to have caffeine, alcohol, or other stimulating beverages. The influence of stimulants like caffeine, alcohol, and other substances on creative brain dynamics is another under-researched area. While some studies have investigated the impact of cognitive and affective stimulation on creativity [such as pain ( Gubler et al., 2022 , 2023 )], more extensive research is needed. The study concerning environmental factors like temperature, humidity, and lighting, has been noted to significantly influence creativity ( Kimura et al., 2023 ; Lee and Lee, 2023 ). Investigating these environmental aspects could lead to more conclusive findings. Understanding these variables related to participants and their surroundings will enable more holistic and comprehensive creativity studies.

4.3.1 Advantages and disadvantages of EEG being used in design creativity experiments

As previously discussed and generally known in the neuroscience research community, EEG stands out as a simple and cost-effective biosignal with high temporal resolution, facilitating the exploration of microseconds of brain dynamics and providing detailed insights into neural activity, which was summarized in Balters and Steinert (2017) and Soroush et al. (2018) . However, despite its advantages in creativity experiments, EEG recording is prone to high levels of noise and artifacts due to its low amplitude and bandwidth ( Zangeneh Soroush et al., 2022 ). The inclusion of physical movements in design creativity experiments further increases the likelihood of artifacts such as movement and electrode replacement artifacts. Additionally, it is essential to acknowledge that EEG does have limitations, including relatively low spatial resolution. It also provides less information regarding brain behavior compared to other methods such as fMRI which provides detailed spatial brain activity.

4.3.2 EEG processing and data analysis

In design creativity experiments, EEG preprocessing is an inseparable phase ensuring the quality of EEG data in design creativity experiments. Widely employed artifact removal methods include frequency-based filters and independent component analysis. Unfortunately, not all studies provide a detailed description of their artifact removal procedures ( Zangeneh Soroush et al., 2022 ), compromising the reproducibility of the findings. Moreover, while there are standard evaluation metrics for assessing the quality of preprocessed EEG data, these metrics are often overlooked or not discussed in the included papers. It is essential to note that EEG preprocessing extends beyond artifact removal to include the segmentation of unstructured EEG data into well-defined structured EEG windows each of which corresponds to a specific cognitive task. This presents a challenge, particularly in loosely controlled experiments where the cognitive activities of designers during drawing tasks may not be clearly delineated since design tasks are recursive, nonlinear, self-paced, and complex, further complicating the segmentation process ( Nguyen and Zeng, 2012 ; Yang et al., 2022 ).

EEG analysis methods in creativity research predominantly utilize frequency-based analysis, with the alpha band (particularly the upper alpha band, 10–13 Hz) being a key focus due to its effectiveness in capturing various phases of creativity, including divergent and convergent thinking. Across studies, a consistent pattern of decreases in EEG power during design creativity compared to rest has been observed in the low-frequency delta and theta bands, as well as in the lower and upper alpha bands in bilateral frontal, central, and occipital brain regions ( Fink and Benedek, 2014 , 2021 ). This phenomenon, known as task-related desynchronization (TRD), is a common finding in EEG analysis during creativity tasks ( Jausovec and Jausovec, 2000 ; Pidgeon et al., 2016 ). A recurrent observation in numerous studies is the link between alpha band activity and creative cognition, particularly original idea generation and divergent thinking. Alpha synchronization, especially in the right hemisphere and frontal regions, is commonly associated with creative tasks and the generation of original ideas ( Rominger et al., 2022a ). Task-Related Power (TRP) analysis in the alpha band is widely used to decipher creativity-related brain activities. Creativity tasks typically result in increased alpha power, with more innovative responses correlating with stronger alpha synchronization in the posterior cortices. The TRP dynamics, marked by an initial rise, subsequent fall, and a final increase in alpha power, reflect the cognitive processes underlying creative ideation ( Rominger et al., 2018 ). Creativity is influenced by both cognitive processes and affective states, with studies showing that cognitive and affective interventions can enhance creative cognition through stronger prefrontal alpha activity. Different creative phases (e.g., idea generation, evolution, evaluation) exhibit unique EEG activity patterns. For instance, idea evolution is linked to a smaller decrease in lower alpha power, indicating varying attentional demands ( Fink and Benedek, 2014 , 2021 ; Rominger et al., 2019 , 2022a ; Jia and Zeng, 2021 ).

Hemispheric asymmetry plays a crucial role in creativity, with increased alpha power in the right hemisphere linked to the generation of more novel ideas. This asymmetry intensifies as the creative process unfolds. The frontal cortex, particularly through alpha synchronization, is frequently involved in creative cognition and idea evaluation, indicating a role in top-down control and internal attention ( Benedek et al., 2014 ). The parietal cortex, especially the right parietal cortex, is significant for focused internal attention during creative tasks ( Razumnikova, 2004 ; Benedek et al., 2011 , 2014 ).

EEG phase locking is another frequently employed analysis method. Most studies have focused on EEG coherence, EEG power and frequency analysis, brain asymmetry methods (hemispheric lateralization), and EEG temporal methods ( Rominger et al., 2020 ). However, creativity, being a higher-order, complex, nonlinear, and non-stationary cognitive task, suggests that linear and deterministic methods like frequency-based analysis might not fully capture its intricacies. This raises the possibility of incorporating alternative, specifically nonlinear EEG processing methods, which, to our knowledge, have been sparingly used in creativity research ( Stevens and Zabelina, 2020 ; Jia and Zeng, 2021 ). Additional analyses such as wavelet analysis, brain source separation, and source localization hold promise for future research endeavors in this domain.

As mentioned in the previous section, most studies have considered participants without their cognitive profile and characteristics. In addition, the included studies have chosen two main approaches including traditional statistical analysis and machine learning methods ( Goel, 2014 ; Stevens and Zabelina, 2020 ; Fink and Benedek, 2021 ). It should be noted that almost all of the included studies have employed the traditional statistical methods to examine their hypotheses or explore the differences between participants performing creativity tasks ( Fink and Benedek, 2014 , 2021 ; Rominger et al., 2019 , 2022a ; Stevens and Zabelina, 2020 ; Jia and Zeng, 2021 ).

Individual differences, such as intelligence, personality traits, and humor comprehension, also affect EEG patterns during creative tasks. For example, individuals with higher monitoring skills and creative potential exhibit distinct alpha power changes during creative ideation and evaluation ( Perchtold-Stefan et al., 2020 ). The diversity in creativity tasks (e.g., AUT, TTCT, verbal tasks) and EEG analysis methods (e.g., ERD/ERS, TRP, phase locking) used in studies highlights the methodological variety in this field, emphasizing the complexity of creativity research and the necessity for multiple approaches to fully grasp its neurocognitive mechanisms ( Goel, 2014 ; Gero and Milovanovic, 2020 ; Rominger et al., 2020 ; Fink and Benedek, 2021 ; Jia and Zeng, 2021 ).

In statistical analysis, studies often assess the differences in extracted features across different categories. For instance, in a study ( Gopan et al., 2022 ), various features, including nonlinear and temporal features, are extracted from single-channel EEG data to evaluate levels of Visual Creativity during sketching tasks. This involves comparing different groups within the experimental population based on specific features. Notably, the traditional statistical analyses not only provide insights into differences between experimental groups but also offer valuable information for machine learning methods ( Stevens and Zabelina, 2020 ). In another study ( Gubler et al., 2023 ), researchers conducted statistical analysis on frequency-based features to explore the impact of experimentally induced pain on creative ideation among female participants using an adaptation of the Alternate Uses Task (AUT). The analysis involved examining EEG features across channels and brain hemispheres under pain and pain-free conditions. Similarly, in another study ( Benedek et al., 2014 ), researchers conducted statistical analysis on EEG alpha power to investigate the functional significance of alpha power increases in the right parietal cortex, which reflects focused internal attention. They found that the Alternate Uses Task (AUT) inherently relies on internal attention (sensory-independence). Specifically, enforcing internal attention led to increased alpha power only in tasks requiring sensory intake but not in tasks requiring sensory independence. Moreover, sensory-independent tasks generally exhibited higher task-related alpha power levels than sensory intake tasks across both experimental conditions ( Benedek et al., 2011 , 2014 ).

Although most studies have employed statistical measures and analyses to investigate brain dynamics in a limited number of participants, there is a considerable lack of within-subjects and between-subjects analyses ( Rominger et al., 2022b ). There exist several studies which differentiate the brain dynamics of expert and novice designers or engineering students in different fields ( Vieira et al., 2020c , d ); however, more investigations with a larger number of participants are required.

While statistical approaches are commonly employed in EEG-based design creativity studies, there is a notable absence of machine learning methods within this domain. Among the included studies, only one ( Gopan et al., 2022 ) utilized machine learning techniques. In this study, statistical and nonlinear features were extracted from preprocessed EEG signals to classify EEG data into predefined cognitive tasks based on EEG characteristics. The study employed machine learning algorithms such as Long Short-Term Memory (LSTM), Support Vector Machines (SVM), and k-Nearest Neighbor (KNN) to classify EEG samples. These methods were utilized to enhance the understanding of the relationship between EEG signals and cognitive tasks, offering a promising avenue for further exploration in EEG-based design creativity research ( Stevens and Zabelina, 2020 ).

4.4 Research gaps and open questions

In this review, we aimed to empower readers to decide on experiments, EEG markers, feature extraction algorithms, and processing methods based on their study objectives, requirements, and limitations. However, it is essential to acknowledge that this review, while valuable in exploring EEG-based creativity and design creativity, has certain limitations which are summarized below:

1. Our review focuses on just the neuroscientific aspects of prior creativity and design creativity studies. Design methodologies and creativity models should be reviewed in other studies.

2. Included studies have employed only a limited number of adult participants with no mental or physical disorder.

3. Most studies have utilized fNIRS or EEG as they are more suitable for design creativity experiments, but we only focused on EEG based studies.

According to what was discussed above, it is obvious that EEG-based design creativity studies have been quite recently introduced to the field of design. This indicates that research gaps and open questions should be addressed for future studies. The following provides ten open questions we extracted from this review.

1. What constitutes an optimal protocol for participant selection, creativity task design, and procedural guidelines in EEG-based design creativity research?

2. How can we reconcile inconsistencies arising from variations in creativity tests and procedures across different studies? Furthermore, how can we address disparities between findings in EEG and fMRI studies?

3. What notable disparities exist in brain dynamics when comparing different creativity tests within the realm of design creativity?

4. In what ways can additional physiological markers, such as ECG and eye tracking, contribute to understanding neurocognition in design creativity?

5. How can alternative EEG processing methods beyond frequency-based analysis enhance the study of brain behavior during design creativity tasks?

6. What strategies can be employed to integrate combinational methods like EEG-fMRI to investigate design creativity?

7. How can the utilization of advanced wearable recording systems facilitate the implementation of more naturalistic and ecologically valid design creativity experiments?

8. What are the most effective approaches for transforming unstructured data into organized formats in loosely controlled creativity experiments?

9. What neural mechanisms are associated with design creativity in various mental and physical disorders?

10. In what ways can the application of advanced EEG processing methods offer deeper insights into the neurocognitive aspects of design creativity?

5 Conclusion

Design creativity stands as one of the most intricate high-order cognitive tasks, encompassing both mental and physical activities. It is a domain where design and creativity are intertwined, each representing a complex cognitive process. The human brain, an immensely sophisticated biological system, undergoes numerous intricate dynamics to facilitate creative abilities. The evolution of neuroimaging techniques, computational technologies, and machine learning has now enabled us to delve deeper into the brain behavior in design creativity tasks.

This literature review aims to scrutinize and highlight pivotal, and foundational research in this area. Our goal is to provide essential, comprehensive, and practical insights for future investigators in this field. We employed the snowball search method to reach the final set of papers which met our inclusion criteria. In this review, more than 1,500 studies were monitored and assessed as EEG-based creativity and design creativity studies. We reviewed over 120 studies with respect to their experimental details including participants, (design) creativity tasks, EEG analyses methods, and their main findings. Our review reports the most important experimental details of EEG-based studies and it also highlights research gaps, potential future trends, and promising avenues for future investigations.

Author contributions

MZ: Formal analysis, Investigation, Writing – original draft, Writing – review & editing. YZ: Conceptualization, Funding acquisition, Methodology, Project administration, Resources, Supervision, Writing – review & editing.

The author(s) declare financial support was received for the research, authorship, and/or publication of this article. This work was supported by NSERC Discovery Grant (RGPIN-2019-07048), NSERC CRD Project (CRDPJ514052-17), and NSERC Design Chairs Program (CDEPJ 485989-14).

Conflict of interest

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Publisher’s note

All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.

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Keywords: design creativity, creativity, neurocognition, EEG, higher-order cognitive tasks, thematic analysis

Citation: Zangeneh Soroush M and Zeng Y (2024) EEG-based study of design creativity: a review on research design, experiments, and analysis. Front. Behav. Neurosci . 18:1331396. doi: 10.3389/fnbeh.2024.1331396

Received: 01 November 2023; Accepted: 07 May 2024; Published: 01 August 2024.

Reviewed by:

Copyright © 2024 Zangeneh Soroush and Zeng. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY) . The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

*Correspondence: Yong Zeng, [email protected]

Disclaimer: All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article or claim that may be made by its manufacturer is not guaranteed or endorsed by the publisher.

Category 11 minutes read

What is an Infographic? Types, Design Tips, Templates

April 18, 2022

If you’re looking for a way to visually showcase a lot of data, tell a complex story, or flex your creative muscles, then you might want to consider creating an infographic.

We live in an age of constant information gathering, processing, and sharing. There are around 2.5 quintillion bytes of data created each day and they are growing at an overwhelming rate as our world becomes more and more digitalized. Infographics help us grapple with this mind-blowing amount of information and represent it in a compelling way. They’re also just a great tool for content marketing.

In this article, we’ll explore the definition of an infographic, describe various use cases, and teach you how to make your own.

What is an Infographic?

First things first – exactly what is an infographic? An infographic is a multimedia graphic that helps you easily share information through a visually stimulating design. It is a way to visualize a concept or a compelling story via creative juxtapositions, graphs, diagrams, and illustrations. 

Why are Infographics Important?

Research has estimated that around 65% of people are visual learners. These people process images about 60,000 times faster than text. This is why infographic design is now a popular means to convey detailed information. 

A a powerful tool for disseminating information, infographics are widely used in science and engineering, medical research, visual communication platforms, and online learning to capture attention and aid in comprehension.

With so many visual learners, and so much data readily available, there are a ton of ways to use infographics. Scientists represent general statistical information and significant data via infographic design. M arketing strategists use infographics to increase audience engagement and build brand awareness. Educators think that infographics are a creative way to break down a complex, multifaceted topic while giving students an enhanced learning experience. Infographics are also used to raise public awareness, discuss the severity of an issue and what should be done to help. 

What Are the Types of Infographics?

Infographics vary based on their purpose and the individual designer. However, there are few common types of infographics. If you’re considering designing an infographic, consider any of these design styles. The actual visual elements can vary, but they’ll all help you tell your story in an eye-catching way.

Pro tip: keep in mind the platforms that you’ll publish your content on when selecting which of these infographic examples will work best for you.

 1. A data-centric infographic emphasizes multiple datasets and important statistics. This informational infographic is designed to make dense, intricate data easier to understand. Use this type of infographic to visualize survey results or represent data from multiple sources. This infographic design should focus on keeping the message clear and concise via a combination of text , charts (tree-bundles, vector illustration, diagrams, pie-charts, etc), and images.

2. Timelines are very popular infographics used to visualize the history of something or explain how a topic changes over time. A timeline infographic design creates a clear picture for the viewer of exactly what happened and when. While you can get creative with these, it’s best to stick to a visually chronological ordering. Showcase relevant events on the timeline by using lines, images, clipart, labels, curved text , contrasting fonts, and color gradients.

3. Comparison infographics are the best infographics to compare and contrast varying topics. Typically, comparison infographics are split down the middle vertically or horizontally, with one option on each side. This enables the viewer to visually see the difference between two or more things.

4. Hierarchical infographics organize information into different levels and display the interconnectedness of these levels. Don’t worry if this sounds complex – you’ve likely seen them before in the form of a pyramid chart or flowchart. One of the famous infographic examples is seen in the food pyramid.

5. Infographic resumes are designed to accompany traditional resumes and are very popular. Visual resumes are the best infographics to help you stand out from the crowd by displaying your experience, skills, and goals via your customized line graphs, word clouds, images, logos, social media icons, and more. Consider using a base like the below and filling in key resume data points for an eye-catching infographic resume that you can share on your digital portfolio .

6. Flowchart infographics are used to answer a specific question by displaying several options and revealing the right answer. They are also used to show how a topic splits off or grows. This type of infographic design is commonly used by educators in the classroom and in popular lifestyle magazines. Consider cropping photos and adding them into the flowchart to make this typically text-heavy infographic design more visually appealing.

7. List infographics are one of the most popular types of infographics due to their versatility. They enable you to skim content, while still clearly displaying the overall message. You can also play around with bright colors or a festive color palette to make each number in the list visually pop off of the page.

Pro tip: It doesn’t need to be numbered to be a list! Consider creating your own thematic stickers to use as icons instead of bullet points to connect your list to the larger design theme.

8. Process infographics show how to do something in simple numbered steps. These are the best infographics to use if you need to visualize a specific process spanning everything from DIY projects to how to clean up the chemistry lab. They’re visually straightforward for a specific reason. Most process infographics are used to teach or reinforce a new topic and follow a top-to-bottom or left-to-right flow.

9. Photographic infographics use images to visualize real-life concepts or tell a story in a memorable striking way. They can be illustration-based or photo-based. Consider these as a more data-heavy version of a text collage .

What Should an Infographic Include?

Now that you’ve selected the type of infographic you want to use, it’s time to consider the visual theme, style, and design elements that will help make the infographic unique. Your infographic design should include unifying graphic elements such as images, icons, and recurring shapes to make data more engaging to viewers. You should also consider a uniform color scheme to tie it all together.

The best infographics have a balance of visuals and text so pick them wisely. Your text should consist of carefully chosen words that emphasizes key ideas clearly and succinctly. The negative space, which is the space void of images or text, is as important as your choice of colors and fonts. Don’t be afraid to use negative space on your infographic; it helps your reader focus their attention on the key visuals.

Pro tip: If you’re just getting started with graphic design, here are our top 12 tips to upgrade your infographic design skills .

What’s the Story Behind An Infographic?

No matter how spectacular your infographic design is, it is the story behind it that matters most. The best infographics combine strong visual appeal with effective presentation of information, unveiling intricate stories lurking in the data. So, the accuracy, rigor, depth, and clarity of the data – where it comes from and what it represents – should be your primary concern when creating your content. 

As Alberto Cairo, designer and visual journalism professor, famously said, “Information graphics should be aesthetically pleasing but many designers think about aesthetics before they think about structure, about the information itself, about the story the graphic should tell.”

Tips for Creating Effective Infographics

1) ask yourself why, go ahead – take out a pen and physically write down answers to the following questions: why am i creating an infographic what am i trying to accomplish with an infographic who is my target audience what do i want the viewer to think, feel, and do after seeing it on what platform will i share the infographic , 2) look into your story, define the so-called “burning point” at the core of your story which makes your content unique. this will likely turn in to the focal point of your infographic.write a descriptive infographic title that ties your story together.   , 3) search for the data, the cold hard data that speaks for itself and backs up your story. remember that choosing which data to use is also a moral act. consider your data source and only use data that comes from an unbiased third-party source. don’t forget to cite your sources in the infographic to show the credibility of your story and build trust. this is commonly done in smaller font on the bottom or top of the infographic., 4) visualize your data.

Select one of the infographic types that best suits your story, data, target audience, and distribution platform. Will your story benefit from comparison, chronological analysis, or from numbered list? How can you capture and hold your audience’s attention? Are you into static, animated, or interactive infographics?

5) Style is key

Is your infographic elegant and descriptive; informative and analytical, or humorous and playful? This will come into play when designing your visual framework or selecting a template.

6) Create an infographic from scratch or choose a template

With so many infographic examples, the world is your oyster. You can use a template or create a design from scratch using charts (bubble, column, pyramid, bar charts, flowchart), shapes, pictograph, lists, tables, stickers, clip art, diagrams, lines, timelines, etc. Don’t forget to play with text styles, too.

7) Input your data

Utilize alignment, repetition, and consistency. Remember to give visual weight to the “burning point” or key takeaway of your story.

8) Make it shareable

Consider sizing dimensions for your infographic so it can easily be viewed and shared across the web.

Infographic Templates

Do you want to use a ready-made infographic template? There are so many amazing templates packed with creative design layouts, shapes, and professional graphics. You can also check out Picsart’s templates and easily customize them by adding your data. We’ll show you how to make a unique infographic from a template in the web-based tutorial below. 

How Do You Create an Infographic?

With the help of a rich array of graphic design tools found in creative platforms, there is virtually no limit to what you can do with data visualization. You can apply your favorite fonts, text styles, and create an eye-catching color palette from an image .

Here’s how to create an infographic in Picsart. We’ll show a template-based tutorial for the web editor and a custom tutorial for the app to show how you can design it both ways.

If designing on web:

1) Open the  Picsart web editor and start a new project.

2) Select Templates in the left panel toolbar and search for infographic. Select your favorite one. Don’t worry – you can customize it for your own design needs by clicking on various layers in the right toolbar.

3) Remove, reposition, resize, or adjust the layers that you don’t need. You can also add additional designs using the Text , Stickers , and Elements tools on the right panel. Remember that you can change the size, font, color, and more of the text by clicking on the layer.

4) When you’re done with your design, click on Export to download and save your infographic design. Here you can also name the file, change the file format, and upscale if needed for printing.

If designing on the mobile app:

1) Open the Picsart app and tap on the plus sign (+) to start a new project. Scroll down to Color Backgrounds to select a blank canvas color. 

2) Set your infographic size by picking any one of the common preset sizes in the bottom toolbar or inputting a custom dimension. Press Apply to save your new canvas size.

3) Use the tools in the Editor toolbar to add Shapes , Text , and Stickers to get a completed infographic look.

4) When done, tap Next then Save and Share your final design.

Whether you choose a ready-made template or create your own, Picsart offers simple, easy to use tools to transform your data into a visually stimulating, cohesive design enabling you to deliver information in enthralling ways. Consider visualizing percentages with creative pie charts and transforming numerical data into a bar graph. Use icons and custom stickers instead of bullet points, break up your story into sections with backgrounds, and play with shapes and color treatments. The possibilities are truly endless!

Create at the Speed of Culture

Picsart is a full ecosystem of free-to-use content, powerful tools, and creator inspiration. With a billion downloads and more than 150 million monthly active creators, Picsart is the world’s largest creative platform. Picsart has collaborated with major artists and brands like BLACKPINK, Taylor Swift, the Jonas Brothers, Lizzo, Ariana Grande, Jennifer Lopez, One Direction, Sanrio: Hello Kitty, Warner Bros. Entertainment, iHeartMedia, Condé Nast, and more.   Download the app or start editing on web  today to enhance your photos and videos with thousands of quick and easy editing tools, trendy filters, fun stickers, and brilliant backgrounds. Unleash your creativity and   upgrade to Gold  for premium perks!

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Designing 3D-printed concrete structures with scaled fabrication models

• Research article
• Open access
• Published: 08 August 2024
• Volume 3 , article number  28 , ( 2024 )

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• Yefan Zhi 1 ,
• Teng Teng 1 &
• Masoud Akbarzadeh   ORCID: orcid.org/0000-0002-6402-615X 1 , 2  

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This article proposes using scaled fabrication models to assist the design research of 3D-printed discrete concrete structures where full-scale fabrication tests are costly and time-consuming. A scaled fabrication model (SFM) is a scaled model 3D-printed the same way as in actual construction to reflect its fabrication details and acquire alike layer line textures. The components of a 1:10 SFM can be eas- ily produced by consumer-level desktop 3D printers with minimal modification. SFMs assist the design communication and make possible quick tests of dis- tinct fabrication designs that are hard to assess in digital modeling [Response to 1.1] during the conceptual design phase. A case study of a discrete compression- dominant funicular floor derived from graphic statics is presented to illustrate the contribution of SFM to the design research of force-informed toolpathing where the printing direction of a component is aligned to the principal stress line. The design iterations encompass a sequence of component, partial, and full model SFM printing tests to explore and optimize the fabrication schemes where par- allel, non-parallel, and creased slicing methods to create toolpaths are compared and chosen to adapt different discrete components.

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1 Introduction

Architectural additive manufacturing, or 3D printing revolutionizes the way build- ings can be designed and constructed. The technology’s ability to create freeform, complex geometry gives architects a higher degree of freedom to realize innovative designs that could either enhance the structural performance or produce unique aes- thetics (Khoshnevis, 2004 ; Paolini et al., 2019 ; van Woensel, van Oirschot, Burgmans, Mohammadi, & Hermans, 2018 ). To explore its strength in cre- ating large-scale efficient structures (walls, beams, bridges, etc.), we are looking into discrete systems where each component is printed separately and then assembled (Fig.  1 ).

[Response to 2.2] Discrete 3D printed architectural systems: ( a ) Post-tensioned concrete beam (Vantyghem et al., 2020 ); ( b ) Multi-span post-tensioned concrete bridge (Ahmed, Wolfs, Bos, & Salet, 2022 ); ( c ) Post-tensioned concrete bridge (Ooms et al., 2022 ); ( d ) Unreinforced concrete masonry footbridge (Bhooshan, 2022 ); ( e ) Post-tensioned concrete bridge (Li et al., 2024 ); and ( f ) Post-tensioned concrete pavilion (Wu et al., 2022 )

While 3D-printed discrete systems provide compelling construction solutions in the cases of Fig.  1 , they also bring challenges to aspects such as discretization design, toolpath rationalization, joint design, and material deposition control which require careful investigations from the designers. Digital visualization and simulation of 3D printed systems have been a primitive tool for them to rationalize the fabrication design. Still it provides limited information on the fabrication challenges and can not predict possible production defects at early stages. On the other hand, a complete full-scale construction test involves the preparation and transportation of concrete, the printing of the components in factories or labs, component transportation, and in-situ assembly (Xiao, Ji, et al., 2021 ). The significant time, material, energy, and labor costs restrict architects and engineers from efficiently conducting tests essential to design development and rationalization.

1.1 3D-printed smooth models

While current commercial/consumer-level desktop 3D printing is extensively exploited as a way to produce physical models for design research and presentation purposes (Jain & Kuthe, 2013 ), there is a mismatch between that and the construction-scale concrete 3D printing paradigm for producing architectural structures (Bos et al., 2016 ; Gosselin et al., 2016 ).

[Response to 2.1] Desktop 3D printers typically use thermoplastic filaments such as PLA, PET-G, and ABS and extrude at a fine resolution of around 0.2 mm layer thickness to produce models with smooth surfaces. They recreate the desired shapes but do not demonstrate the fabrication details and layer line textures inherent to actual construction scale printing.

The toolpaths generated by commercial slicing software (e.g. Cura, Slic3r) for these desktop thermoplastic printers are often far different from those of the structural components printed in industrial gantry or robotic setups. They make frequent start- and-stops in extrusion and print with high overhang angles incompatible with concrete printing. [Response to 2.1] Thus the successful production of smooth models does not support the viability of the actual fabrication proposal. Furthermore, one component can be converted into different toolpaths under different printing schemes (orientation, slicing method, sectional dimensions, etc.) in construc- tion and results in different forms. Such variations cannot be reflected by desktop models as tuning their printing parameters only results in minimal differences. Thus slicing and printing with current desktop 3D printers can contribute little to design iterations related to the actual fabrication.

1.2 Design with scaled fabrication models

To facilitate and accelerate the design iterations in 3D-printed discrete system con- struction, this study proposes a design-to-fabrication research paradigm that utilizes 3D-printed scaled fabrication models instead of the [Response to 2.1] smooth models mentioned above. A scaled fabrication model (SFM) is a scaled model that is fabricated the same way as in real construction. In our case, it is a model 3D printed using the toolpath that would also apply to the actual production, and its sectional dimensions (width and height) are also decided by scaling the actual production. SFMs effectively contribute to design decisions for the following reasons:

SFMs are easy to produce. As will be shown in Sect.  2 , the setup is easily established by modifying a commercially available desktop printer. Toolpaths for construction-scale gantry 3D printers can be easily scaled down to adapt to desk- top material extrusion printers by the scale of 1:10 and vice versa, requiring little additional effort in preparing the machine codes for printing. Printing in the desk- top scale uses only one person and significantly saves time, material, energy, and labor compared to the construction scale.

Unlike computer simulations, physical mockups can provide tactile feedback and reveal subtleties related to spatial relations, aesthetics, and material behaviors that may not be fully captured digitally (Viswanathan & Linsey, 2011 ). Compared with [Response to 2.1] smooth models, SFMs also carry the same texture as actual construction-scale prints since the geometric parameters are scaled down from the construction scheme. It enables fast realizations of design prototypes at a low cost. Architects, engineers, and researchers can fabricate and test numerous designs and share them with clients in a matter of days if needed (Sharif & Gentry, 2015 ).

Mocking up architectural structures on a smaller scale using desktop 3D printers could also provide a tangible method of testing and optimizing the construction pro- cess before actual implementation, complementing computer simulations (De Luca et al., 2006 ). [Response to 1.4] In preparing SFMs instead of smooth models, designers are asked practical questions such as what is the sectional dimen- sion and how the prints fit with joinery and reinforcements. By exploring different printing schemes of the SFM, one effectively tests that of the actual production in terms of slicing and toolpathing options as well as sectional dimension choices. The concrete mixture consists of coarse aggregates and is hard for small-scale printing. Therefore, SFMs use thermoplastic filaments or clay to replicate the geometri- cal parameters on the desktop scale. Although the material property is different, SFM can reveal fabrication defects related to the geometric form such as collision, extreme overhang, extreme angles, etc. It is a handy troubleshooting tool for the fabrication rationalization of the discrete structural components. Furthermore, it also offers an opportunity to test the tectonics of the structural assembly.

The SFM serves as an intermediate tool from geometry-centered design to fabrication-oriented design. [Response to 1.1] It contributes to the conceptual design phase by outlining the limits of 3D-printed concrete structures. Once a design has been validated using the SFM method, it is anticipated that full-scale tests will expe- rience fewer errors. However, we acknowledge that aspects related to the construction such as the lifting of the components and assembly errors might not be fully cap- tured in the SFM stage. Table 1 showcases a series of models produced for the design and fabrication research of a structural component in a parallel study where the pro- posed method plays a vital role. Between the plain [Response to 2.1] smooth model (Table  1 a) and the final production (Table  1 e), three different specifications are adopted for scaled model productions. The geometry has been adjusted through the sequential process.

The 1:10 SFM (Table  1 b) uses thermoplastic filaments or clay and is sliced and printed as proposed in Sect.  2 . It is a 1:10 scaled model whose sectional dimension

(3 × 1 mm) is determined by that of the original concrete production (30 × 10 mm) and thus truthfully reflects the final fabrication details and layer line textures.

The 1:10 material model (Table  1 c) utilizing desktop concrete printing is also con-

venient to produce and helps to understand the printability limits in concrete 3D printing. It is also a strong candidate for strength tests. However, the coarse aggre- gates in the concrete mixture make it impossible to print with a to-scale section of 3 × 1 mm. It thus does not reflect the final appearance of the print.

The 1:2 fabrication model (Table  1 d) uses the same print section as the full-scale production. The refined results indicate the viability of the final production. The model can be used for strength tests as well as loading tests when assembled. How- ever, it is a practical option only when the design is finalized in all aspects due to the costly printing process and should supersede the design iterations using SFMs.

This article focuses on the method of scaled fabrication model (Table  1 b) which is a crucial step in moving from geometrical design to fabrication research. It gath- ers useful information related to both aesthetics and fabrication and paves way for larger tests suitable for structural performance tests, further underscoring the real-world applicability of our research. This intermediate process materializes the complex design of architectural components via additive manufacturing, streamlining the design-fabrication procedure.

1.3 Scope of the study

This article explores the advantages of designing 3D-printed concrete structures with SFM. Firstly, it will offer an accessible and functional setup for printing SFM on a 1:10 scale. Secondly, it will illustrate the SFM-assisted design strategy using the case study of a compression-dominant discrete funicular floor designed for concrete 3D printing construction. The case study will involve design iterations that explore the realization of efficient toolpaths based on the form-finding results from graphic statics. Different slicing methods, as the core of fabrication rationalization, will be investigated. Lastly, an operative framework will be summarized.

2 Methodology

2.1 desktop-scale printing setup for sfm.

Deskstop-scale SFM can be handily produced by our accessible, affordable, and ver- satile setup. Commercial/consumer-level 3D printers usually print at fine resolutions to create smooth surfaces as they are desired for general purposes. The nozzles used usually have a 0.4 mm diameter with a cross-section of 0.13 mm 2 and print at the layer height between 0.1 and 0 . 3 mm. On the other hand, typical concrete 3D printing uses a layer height of 10 mm. We propose that the scale of SFM be 1:10 as smaller than that the layer line textures will be hard to capture and bigger than that it would be hard to print with regular filaments of 1.75 mm diameter. On the 1:10 scale, the 30 × 10 mm concrete section becomes 3 × 1 mm with an area of 3 mm 2 which is too large for the 0.4 mm nozzle. This issue is solved by simply replacing the default noz- zle with a commercially available 1 mm diameter nozzle with a cross-section of 0.79 mm 2 . Niknafs Kermani, Advani, and F´erec ( 2023 )’s simulation suggests that 1 mm layer height complies with 1 mm nozzle diameter.

Our setup is a Creality CR-10 printer with a 1 mm nozzle replacement. CR-10 has a remote extruder fixed on the gantry beam. We replaced it with a direct extruder attached to the hotend which improves material flow control and minimizes issues from retraction in our case of thick layer printing. As the sectional area of the print increases, both the extrusion speed and the nozzle travel speed should slow down to allow successful feeding and deposition of the filament. According to our printing experiments, the print reaches the best extrusion quality when the travel speed of the nozzle is 1.6 mm/sec.

The material of choice for our experiment is polyethylene terephthalate glycol- modified (PET-G) filament. Other thermoplastic filaments also fit our setup. We are also capable of printing clay with the same sectional dimensions using a syringe and auger feeder.

2.2 Design with graphic statics

In this research, the advantages of desktop-scale SFM is illustrated in a case study of a funicular floor system, designed utilizing graphic statics. Graphic statics is the study of efficient structural forms utilizing graphical representations (Akbarzadeh, 2016 ). Polyhedron-based 3D graphic statics, as implemented in this research, makes use of reciprocal form and force diagrams consisting of vertices, edges, and polyhedral cells. It offers an effective form-finding method for funicular structures whose members receive primarily axial forces under a proposed loading scenario.

Graphic statics can assist in the form-finding of funicular systems. While the form diagrams solved by graphic statics are primarily bar-node models with linear elements (Fig.  2 a, b), research shows that they can be adapted as surface continuum models for ease of fabrication using sheet-based materials (Fig.  2 c, d). Graphic statics also helps the design process of complex freeform concrete structures by offering an illustrated operation framework closely linked with fabrication methods while reflecting struc- tural properties associated with the theory of plasticity (Schwartz, 2018 ). In addition, locations and magnitudes of the principal stress are annotated by the edges of the form diagram. Thus we will be able to reinforce the form accordingly.

Built projects designed with polyhedron-based 3D graphic statics: ( a ) a concrete spatial table (Akbarzadeh et al., 2021 ); ( b ) a concrete pavilion (Bolhassani et al., 2018 ); ( c ) a glass bridge (Lu, Seyedahmadian, et al., 2022 ); and ( d ) a paper bridge (Lu, Alsalem, & Akbarzadeh, 2022 )

Our proposal is in line with these endeavors to push forward the application of graphic statics by materializing certain walls of each polyhedral cell to create 3D- printable surfaces that would take and distribute the forces. By utilizing the notion of SFM, it aims to strengthen the connection between graphic-statics-driven design and materialization.

PolyFrame 2 (Lu, Hablicsek, & Akbarzadeh, 2024 ; Nejur & Akbarzadeh, 2021 ), a plug-in for Rhino (Robert McNeel & Associates, 2023b ) and Grasshopper (Robert McNeel & Associates, 2023a ), is a form-finding software based on the principles of graphic statics. The software takes a polyhedral force diagram as input and generates the reciprocal funicular form diagram using an iterative solver or an algebraic solver. Thus we are able to translate the funicular floor form-finding problem into designing an efficient polyhedral force diagram.

Zheng et al. ( 2020 ) utilizes machine learning to investigate the subdivision of a simple planar force diagram. In his work, the subdivided pattern is extruded to con- struct a 3D polyhedral force diagram, which then populates the efficient funicular form. Our work is based on one of his optimal outcomes which is claimed to reduce material usage by 51 . 7% (Fig.  3 ). Note that the funicular form is materialized into a ribbed floor where the funicular edges are projected to the top plane. The fabrication method of the system is not explored.

The funicular floor based on an optimal subdivision pattern generated through machine learning. Adapted from Zheng, Wang, Qi, Sun, and Akbarzadeh ( 2020 )

In this paper, we propose a compression-only unreinforced discrete system start- ing from the same subdivision force pattern.

[Response to 1.3] The design space covers one column, matching the force diagram explored in Zheng et al. ( 2020 ). When repeating horizontally, the units form an aggregation as a compression-dominant funic- ular floor system. However, tension forces are needed at the boundaries of the floor system. The arrangement of the multi-span floor is beyond the scope of the paper. The one-column unit is fabricated as an SFM in our study. To further rationalize the materialization, the following adjustments are made to the force diagram (Fig.  4 ): The polyhedral cells are split in the middle to form two layers in the form diagram which become respectively the top and bottom edges of the polyhedral cells. The splitter is a sphere so that the polyhedral cells’ vertical edges are gradually slanted towards the center (also seen in Fig.  10 ), similar to an unreinforced masonry vault system. Fur- thermore, the top half is replaced by extruding the new faces in the middle to the top surface so that the faces in between are perpendicular to the XY-plane. Thus the final form’s top edges sit in the same XY-plane and make the cells flat to accommodate the flooring.

The graphic statics approach of form-finding: based on the force diagram (Γ † ), a form diagram is found (Γ). Top: a simple configuration; Bottom: a subdivided and adjusted configuration adopted in this paper

Additional post-processing to the form diagram is needed since the reciprocity determines the directions of the edges but not the length of them in certain configu- rations. The method to finalize this form is known as constraining the force or form diagram (Lu et al., 2024 ; Nejur & Akbarzadeh, 2018 ). We are specifically constrain- ing the bottom 4 and outermost 8 vertices of the funicular form diagram to tailor the bounding dimensions of the system. With the help of the iterative constraint solver of PolyFrame 2, the symmetry of the system is also preserved. The result is a funicular mushroom floor where each column covers a 2.6 × 2.6 m floor space.

Utilizing graphic statics, the form-finding process is direct and fast. It also gives us an opportunity to reinforce the area with maximal axial forces in the funicular form. Later in Fig.  6 we can see the tailored reinforcement acts as an additional layer of rib in the system.

We revisit the ribbed floor system visualized by Fig.  3 . Its simpler version can be seen in buildings where the entire span of the floor is cast in formworks over scaffold- ings. The casting method restricts the floor’s section geometry which can not have cavities. However, with 3D printing the creation of cavities is possible and materi- als can be assigned to the bottom of the funicular cells to better receive compressive forces and enhance the system’s load-bearing capacity.

2.3 Force-informed toolpathing

An advantage of 3D printing is the selective deposition of material according to loading and stress conditions to increase material efficiency. For example, Tam and Mueller ( 2017 ) and Breseghello and Naboni ( 2022 ) assigned material along the stress lines to create efficient funicular forms. In our architectural 3D printing paradigm for producing shells, the deposition of material is also crucial. We propose a two-fold notion of force-informed toolpathing in deciding the printing schemes for compression- dominant components:

Solid printing: A solid geometry can be converted into layered toolpaths in differ- ent orientations. The anisotropic object (Fig.  5 a) has a higher compressive strength in the printing direction (Z axis of the printbed) compared to the other two direc- tions (X, Y) (Ma et al., 2019 ; Xiao, Liu, & Ding, 2021a , 2021b ). Therefore, when the geometry receives predominantly an axial compressive force, its printing direction should align with the stress to maximize the strength of the structure (an example can be seen in Teng, Zhi, Yu, Yang, and Akbarzadeh ( 2023 ) where compressive and tensile forces are both discussed).

Contour printing: In architectural 3D printing, the creation of solid objects is often unnecessary and components are printed in layers as contours with optional infill patterns (Fig.  5 b). The printing direction (Z) should also align with the stress for two reasons. Firstly the solid part has higher compressive strength in the Z direction. Secondly, in the X or Y direction, the geometry only has two continuum walls touching the two contact ends, and the rest material forms caps that do not pick up the compressive force.

Different orientations in ( a ) solid printing and ( b ) contour printing

The efficient toolpathing notion to harness the material printed can be summa- rized as “form follows force”. It directs the decisions in discretization and printing scheme development. The tailored slicing is not reflected in solid models produced by conventional desktop printers and can only be further examined in our proposed SFMs.

2.4 Discretization

The form generated using the graphic statics solver is accompanied by the direction and magnitude of the inner stresses. Following the notion of force-informed toolpathing the printing direction can be assigned aligning with the stress directions in our funic- ular model to enhance their compressive strength. Figure  6 visualizes the cells and how their printing directions are aligned with the force. We also add additional corrugated ribs to the edges with max stresses for reinforcement. The alignment between print- ing and stress directions guides the discretization (Fig.  10 ) and slicing of all 62 pieces that formed the physical model of one column floor. It is further experimented in our rapid SFM production.

Visualization of the assembled system showing the printed layers whose printing directions are aligned with principal stress lines suggested by the funicular form generated by graphic statics. The funicular form is visualized such that the section area is proportional to the axial force magnitude

Another restriction of discrete systems is the size of elements to be manufactured and assembled. Therefore cells are inspected before orientation so that some small units with less axial forces are merged and some big units are split into two, resulting in acceptable and efficient sizes for construction-scale 3D printing.

2.5 Printing schemes

The design proposal employs a gantry printing system with a flat printbed. Therefore, the components are restricted to having flat bottom surfaces. On the other hand, not all surfaces/polysurfaces suit the criteria of being a cap that directs the slicing planes. The capping surface/polysurface should cover roughly the entire geometry to minimize areas acting as side caps that receive forces perpendicular to the printing direction (parallel with the layer plane). To prevent the creation of extremely thin layers that will corrupt the print quality, the angle between the top surfaces and base plane should not be too big, concerning the distance between them.

Figure  7 illustrates the details on determining the slicing method after a princi- pal stress direction is given and the geometry is oriented on the base plane chosen respectively. Three slicing methods are developed:

Parallel Slicing: Conventional slicing where each slicing plane is parallel to the bottom. Most commercial/consumer-level slicing software uses this default method. When dealing with vertically extruded structures it does not take full advantage of 3D printing systems and is thus known as 2.5D printing.

Non-parallel Slicing: The bottom and top faces are not parallel. They intersect at a rotating axis, around which a guide arc L starts from the centroid of the bottom face and ends at the top. The number of layers is calculated using L as the height in parallel slicing. The bottom plane is rotated around the axis to form the slicing planes in between.

Creased Slicing: A polysurface cap is identified. Referring to each face of the poly- surface cap a set of curves are sliced using the non-parallel slicing method. The sets of curves are trimmed and joined to form the final curves. Note that for each sur- face the number of layers may vary. Due to that, the seam points where two sets of trimmed curves are connected are shifted from the location of the polysurface seam in the example shown by Figs. 7 and 8 b.

Left: The computational flowchart for determining slicing types (non-parallel, parallel or creased) and slicing to get the toolpath and GCode; Right: An example of creased slicing

Comparative SFM component printing studies between ( a ) non-parallel and parallel slicing; ( b ) parallel and creased slicing; and ( c ) different non-parallel slicing configurations

The final post-process before printing is to connect the curves between layers and calculate the adaptive extrusion. A simple shell geometry usually has only one closed curve in each layer. They can be organized such that the cycling directions are the same (usually counter-clockwise) and their seam points are aligned to the previous. Based on those organized curves, a continuous curve that preserves the continuous finish of the print can be easily created. For the creased slicing where open curves take place at top layers, U-turn connections between layers are created and certain curves are flipped accordingly.

[Response to 2.4] Adaptive extrusion refers to the local control of extrusion flow rate in the printing process. It has been utilized to achieve variable width for creating undulating surfaces (Yuan, Zhan, Wu, Beh, & Zhang, 2022 ; Zhan, Wu, Zhang, Yuan, & Gao, 2021 ) or avoiding overfills and underfills (Kuipers, Doubrovski, Wu, & Wang, 2020 ). In the method of non-parallel slicing, adaptive extrusion helps realize the print’s variable height. The height at a sample point is locally calculated as the distance from it to the previous slicing plane/polysurface. With the variable layer height information embedded in the toolpath, we can instruct an adaptive extrusion in the GCode to retain the same sectional width of 3 mm by changing the sectional area. Adaptive extrusion is key to matching the printed shell with the input geometry.

2.6 Design iterations with SFM

SFM can be produced once a primitive fabrication design is formed. To effectively visualize the design and test the fabrication details, we propose scaled printing tests in a sequence of three different scopes.

Component printing test: Fig.  8 illustrates how a cell can be assigned different ori- entations and slicing methods. Figure  8 a shows that a triangular piece with a 45° top surface has messy layer lines when printed non-parallelly due to the extreme angle. It can only be successfully printed with parallel slicing. Figure  8 b shows how a com- ponent has poorly continuous top surfaces when it is printed using parallel slicing. However, when the slicing plane is rotated and creased to align with the top sur- faces (also shown in Fig.  7 ), the finish quality is enhanced and the top matches the design more precisely. Figure  8 c shows how different non-parallel slicing configuration raises the problem of extreme overhang and over extrusion. Such results are hard to precisely predict with modeling software and can be only captured by SFMs.

Partial printing test: Fig.  9 gives an example of printing part of the model to test contact surface quality. By checking the connectivity between adjacent components and consulting the force magnitude provided by graphic statics one can find an optimized solution for the discretization and printing scheme design and adjust the original form if necessary.

Full model printing test: After the design proposal has passed the previous two examinations, it is ready for a full model printing and assembly test. Building the SFM model also gives feedback to the construction-scale assembly proposal.

Partial SFM printing and assembly test with two different configurations

With the rapidly produced full model, it is also easier for communication between collaborators and clients.

Figure  10 gives an overview of our method from form-finding to materialization and fabrication of the system. By incorporating graphic statics with 3D printing ratio- nales, it fully demonstrates the potential of SFM in prototyping innovative 3D-printed concrete structural systems.

The proposed workflow from designing to prototyping the 3D-printed structural system

[Response to 1.3] The full model consists of 62 3D-printed components. The pieces are then assembled according to a CNC-milled foam base simulating the scaffolding using glue. The assem- bly sequence of the compression-only system starts from the center, as in a masonry vault structure (Fig.  11 ).

Left: The assembly sequence of the model; Right: The 26 × 26 × 25 cm model with its scale shown

As presented in Table  2 , it is proven that designing 3D-printed concrete structures using SFM is a cost-effective approach. The setup cost of a desktop 3D printer is low. Less material is required to print small-scale models, minimizing material usage and cost. Smaller models also have shorter printing times so that more design iter- ations can be evaluated. Desktop 3D printers also typically consume little energy relative to industrial systems, not to mention the labor saved in processing the mate- rial and maintaining the workspace. Given the low overhead in terms of both economic and material resources needed for desktop-scale SFM versus full-scale fabrication, the proposed method enables an agile design-fabrication process.

The built model (Figs.  12 , 13 ) demonstrates the idea of aligning the form with force and makes possible a close investigation into the strength and limits of such structures. The ribbed part realized by the U-shaped profile of the toolpath adds to both structural strength and the aesthetic of the system as they highlight the idea of “form follows force”.

Physical prototype of the proposed funicular floor system with one column

Top: The proposed system organized by four columns; Bottom: discrete blocks, detail, and top view of the floor

4 Conclusion and future work

4.1 contribution of the study.

The contribution of the study is three-fold. It introduces the concept of SFM as opposed to conventional [Response to 2.1] smooth models and proposes the usage of SFM to bridge the gap between geometrical and fabrication design and streamline the design iterations in 3D-printed discrete concrete structures. It provides an accessi- ble and affordable platform for printing SFMs and presents slicing methods compatible with different geometries.

The case study of the funicular floor showcases the rapid design iterations made possible by producing SFMs on three different scopes: component, partial model, and full model. It sets a paradigm for efficiently rationalizing discrete 3D-printed con- crete structures. The method can be adapted to different design tasks and facilitate communication between architects, engineers, and clients.

By adopting the form-finding approach of polyhedron-based graphic statics and aligning the printing direction with the stress, this case study celebrates the notion of force-informed toolpathing and illustrates SFM’s ability to help realize design research in architectural 3D printing.

4.2 Limitations

[Response to 1.2, 2.3, and 1.4] Currently the application of SFM is limited by the following aspects. The commercially available desktop printers tested in this study have 3 translational degrees of freedom (DOF) while robotic printing systems have 6 DOFs. The SFM method with such printers does not reflect the effect of rotating nozzles, inclined nozzles, and non-standard nozzles seen in robotic printing systems.

Printing schemes can be tested effectively in SFMs to show if they are collision- free, recreate the desired surfaces, and demonstrate consistent layer lines. However, materials used in SFMs and construction-scale concrete printing are different, resulting in different limits in printable overhangs of the toolpaths. In that regard, being able to be printed as SFM, especially using thermoplastic filaments, does not guarantee a successful print on a construction scale. Furthermore, concrete printing typically experiences a 2% linear shrinkage during the curing process (Zhang & Xiao, 2021 ) and is thus suspect to cracking. This issue is not reflected in SFMs. In using SFMs as verification, the designers and engineers need to resort to past experience to prevent such possible defects before moving on to the full scale.

For exploratory design iterations, SFMs are informative companions to designers. However, to facilitate construction tasks, quantitative metrics for evaluating SFM tests need to be established.

4.3 Future work

To further harness the advantages of SFM, this study can be expanded in the following aspects. The slicing and printing methods and the physical setup can be upgraded such that advanced 3D printing methods including non-planar printing (Anton, Skevaki, Bischof, Reiter, & Dillenburger, 2022 ; Mitropoulou, Bernhard, & Dillenburger, 2020 , 2022 ), multi-axis printing (Dai et al., 2018 ; Fang et al., 2020 ; Li et al., 2022 ), and continuous printing (Zhao et al., 2016 ) can also be tested in SFMs. Implementing those methods in SFM verifies their viability in producing 3D-printed concrete structural components.

The method of SFM can be applied to discrete concrete systems with complex fabrication details such as bolts and nuts and post-tensional cables. The interface between the standard metal hardware and the toolpath (for example, conduits for post-tensioning cables in Fig.  1 a, c, d) can be investigated using SFM.

[Changed the order of the two paragraphs to match the Limitations subsection.] Cross-scale quantitative criteria for assessing the printability and finish quality of the components can be developed. To do so, a series of controlled printing experiments can be conducted where the SFMs then undergo numerical examinations using methods such as digital image correlation.

Alternative materials can be investigated in producing SFMs. Different types of thermoplastics, customized concrete recipes, and other materials can be tested and their advantages, limitations, and resemblance to regular concrete can be compara- tively studied. By printing with a material that has the closest density and rheology to concrete, one can effectively test the material behavior of the deposition using SFM and thus further verify the printability of the construction scale components.

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Acknowledgements

This research was supported by the Future Eco Manufac- turing Research Grants (NSF FMRG-2037097 CMMI) and Faculty Early Career Development Program (NSF CAREER-1944691 CMMI) of the U.S. National Science Foundation and the Advanced Research Projects Agency–Energy (ARPA-E) Grant of the U.S. Department of Energy (DE-AR0001631) awarded to Dr. Masoud Akbarzadeh.

The authors would like to thank Yi (Simone) Yang for the assistance in model making and Dr. Maximilian E. Ororbia for the writing suggestions.

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Yefan Zhi: Conceptualization, Methodology, Software, Formal analysis, Investigation, Writing—Original Draft, Writing—Review & Editing, Visualization, Project administration. Teng Teng: Conceptualization, Methodology, Writing—Review & Editing. Masoud Akbarzadeh: Conceptualization, Resources, Supervision, Funding acquisition, Writing—Review & Editing.

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15 of the best design portfolio examples

The best design portfolios come in all shapes, sizes and formats.

Getting your portfolio right is vital – it can be the difference between getting hired or not. And there's no end to how creative you can be with your portfolio design. If you need to update yours, looking at a few examples is a good place to start, which is why we've collated this list of the best design portfolios around.

To start building your design portfolio, you need a platform to create it on. Check out our best website builder roundup, or you can head over to the best portfolio templates for pre-existing designs. But for now, just scroll down to indulge in some of the best design portfolios, listed in no particular order.

Top design portfolio examples for inspiration

01. bruno simon.

Paris-based creative developer Bruno Simon has approached his portfolio in an unexpected way. You can actually drive a virtual car between his projects and experience using a keyboard. In 2019, it won Site of the Year at  awwwards , and it's not hard to see why. We wouldn't recommend this type of portfolio to everyone, but if you can make the design of your portfolio show off the skills you want to highlight, then you should.

Gus is a creative strategy company rather than a straight design site, but we think there are plenty of lessons to be learned from its brilliant site. It's cleverly laid out on a grid, and strikes an irreverent tone while giving the reader exactly what they're looking for, with an easy to navigate UX. We particularly like the ' frequently asked questions ' section.

03. Good Habit

London branding and design studio, Good Habit , has a fun and fresh portfolio that beautifully displays its work. A plainer Studio section outlines what the studio does, while the brands section displays projects with large format photography intermixed with sections of texts. It really works.

04. Studio Feixen

This Switzerland based design studio is absolutely jam-packed full of fun and characterful work. Studio Feixen perfectly showcases its vibrant work with a mix-match style portfolio that abstains from a 'less-is-more' approach. Despite the examples being framed in a range of different sized shapes on the portfolio page, the site still looks cohesive.

05. RoAndCo

Founded by creative director Roanne Adams, NYC-based RoAndCo offers beautifully crafted design, branding and creative direction to clients in fashion, beauty, tech and lifestyle. Viewing RoAndCo’s portfolio is an experience in itself, in keeping with the studio's work ethos. Projects are presented in an editorial-like fashion, allowing the viewer to flick through split-screen images, animated web presentations and full-screen video. It's a carefully considered design portfolio and a pleasure to view, whether you're browsing on a computer or a mobile device. 

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06. Robin Mastromarino

Paris-based interface designer Robin Mastromarino employs some neat UI animation touches to keeps things fresh on his design portfolio site. His projects appear as though they're on a wheel, juddering into view, which is an engaging effect. The images in each case study respond to scrolling by warping slightly. It's an effect that we wouldn't recommend for every creative, but for a UI design specialist, this strikes the right note and gives a taster of what the designer can do. 

07. Active Theory

Entering Active Theory 's portfolio website is like visiting a whole other world. It employs a moody, almost cyberpunk aesthetic throughout, and to great effect. From the atmospheric homepage animation with mouse-activated glitch effects to the trippy About page, the setting all gels together to form a cohesive package. The studio keeps things cleaner for its project pages. Each example features a full-screen animation overlaid with a short blurb and relevant links to further information, including detailed case studies hosted on Medium.

08. Raw Materials

Raw Material' s site is a feast for the eyes. The Work section is particularly fun, with more detail on projects shown through diagrams and images. We also like the 3D models in the 'Hello' section, which also appear in 'Contact'. Overall it's a fun fresh site that makes the studio stand out from the crowd.

09. Velvet Spectrum

Velvet Spectrum is the online moniker of visual artist and designer Luke Choice. He shows that simplicity can also make an impact on his homepage, which shows a montage of uber-colourful thumbnails that lead through to visually arresting super-size examples of his work for maximum impact. The black background keeps things clean and helps the work stand out. It makes for a simple but highly effective design portfolio.

10. Locomotive

Locomotive , a studio based in Quebec, Canada, specialises in crafting digital experiences, so it's taken care to make its design portfolio site an all-round delightful and engaging experience. Playful, entertaining animations bring the site to life, and not just on the homepage. It seems like thought and effort has been put into every detail. Little surprises keep the viewer's interest while they browse through the site, making this a perfect example of how animated flourishes can be used effectively without them becoming gimmicky or distracting.

11. Studio Thomas

Named after its two creative directors, Thomas Austin and Thomas Coombes, Studio Thomas in East London creates visual communication for both physical and digital worlds. Its portfolio is a superb example of Brutalist web design with plenty of neat touches. Projects are presented in an orderly but eye-catching way with clear visuals and wireframe models. The site perfectly reflects the studio's explorative and experimental attitude, and it backs up the studio's claim to offer "design for bold brands."

12. Buzzworthy Studio

Describing itself as a "badass digital studio in Brooklyn", Buzzworthy Studio really needed to come up with the goods to back up that claim, and happily, its portfolio does the job. It features dazzling web techniques from the off. Bold typography and animation combine to grab your attention, and a strong eye for aesthetics ensures that viewers stick around to explore all of Buzzworthy's projects. It's one hell of a calling card.

13. Xavier Cussó

This stunning portfolio site for Barcelona-based designer Xavier Cussó was built by Burundanga Studio. It shows off Cussó's work with bold colours, in-your-face typography and practically every animation and parallax scrolling trick in the book. But that doesn't make it feel overloaded. The animation makes and impact and maintains the viewer's attention throughout.

14. Merijn Hoss

Illustrator and artist Merijn Hoss takes a more pared-back approach, but his design portfolio is still very effective. Hoss creates beautifully detailed psychedelic works of art, but his profile presents his work in quite a simple, clean format. It's one of the most traditional approaches we've included on this list of design portfolios and isn't nearly as flashy as some of the previous examples, but it works well because the colourful thumbnails really pop out of the gallery's white background, putting the focus on the artist's work. Click the thumbnails, and large project images and a short description are revealed. Hoss's design portfolio is proof that you don't need all the bells and whistles to make an impact.

15. Malika Favre

Illustrator Malika Favre uses a full-screen edge-to-edge tapestry of thumbnails to entice visitors into viewing her vibrant artwork in more detail. The colours and layout already draw attention, while the arrangement of animated pieces within still artworks serves even more to keep eyes on the screen. Once clicked, the thumbnails reveal a full-screen gallery presentation of the work featured. It's displayed on complementary coloured backgrounds that show off her work to great effect and makes for a bold, colourful presentation that grabs the viewer's attention.

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Ruth spent a couple of years as Deputy Editor of Creative Bloq, and has also either worked on or written for almost all of the site's former and current print titles, from Computer Arts to ImagineFX. She now spends her days reviewing mattresses and hiking boots as the Outdoors and Wellness editor at T3.com, but continues to write about design on a freelance basis in her spare time. 

• Rosie Hilder

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