Home > Vol. 37, No. 1
- Nayeon Kim : Department of Spatial Design and Consumer Studies, College of Human Ecology, Assistant Professor, The Catholic University of Korea, Seoul, Korea
- Seohyeon Chung : Department of Culture and Design Management, Underwood International College, Student, Yonsei University, Seoul, Korea
- Da In Kim : Department of Culture and Design Management, Underwood International College, Student, Yonsei University, Seoul, Korea
Background Human experiences are key considerations in design research and practice. Neuroscience techniques allow quantitative measurement of underlying human neurophysiological responses to design. However, despite the importance of electroencephalography (EEG) in performing such quantification, design experiments have not widely applied EEG, limiting the insights that design researchers can produce. Thus, this paper describes the use of EEG in experimentation in various design fields and suggests its integration into design research.
Methods This study systematically reviewed experimental design research that utilized EEG in various design domains, such as product design or architecture. Twenty-nine papers were selected using the preferred reporting items for systematic reviews and meta-analyses (PRISMA) method. The selected papers were published in peer-reviewed journals between 2012 and 2022, written in English, and were analyzed for their design, variables, EEG tools and indicators, stimuli, experimental settings, analysis methods, and findings. Analysis was applied through a framework, population, intervention, control, outcome, and setting (PICOS) methodology.
Results This paper analyzed EEG-based experiments according to PICOS to provide information about how EEG is used in experimental design research, shedding light on the application of EEG methodology in various design fields, including product design, interior (or architecture) design, and service design. The results show that neuroscience techniques can be used to collect brain data for design research. EEG has been used in various experimental design research fields to explore how an individual user reacts to specific design elements and experience.
Conclusions Neurophysiological data retrieved from experiments can be used to develop evidence-based design strategies to improve the design process and design decision-making. The findings in this study contribute to our understanding of cognitive, emotional, and behavioral responses to design.
Copyright : This is an Open Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License (http://creativecommons.org/licenses/by-nc/3.0/), which permits unrestricted educational and non-commercial use, provided the original work is properly cited.
Recent advances in methodologies and instruments to detect neurophysiological responses enable a design researcher to investigate cognitive functions (Hu & Shepley, 2022; Ball & Christensen, 2019). Brain responses have the potential to increase our understanding of the relationship between human behavioral response and design elements (Gero and Milovanovic, 2020; Vieira et al., 2020).
Specifially, electroencephalography (EEG) records brain electrical activity using electrodes placed on the scalp to capture brain waves from the frontal, parietal, temporal, and occipital cortex (Jaiswal,et al., 2010). Although EEG has considerable potential for deepening our understanding of how people respond to design elements, it has not been extensively applied in experimental design research. Accordingly, comprehensive information regarding application to design is lacking (Kim and Kim, 2022; Borgianni and Maccioni, 2020). A general conclusion about the relationship between EEG, design, and experimental research remains elusive.
This study reviewed and analyzed studies that included EEG as part of their experimental design. This paper further discusses the results of experiments measuring EEG in different brain regions and use of different EEG electrodes to measure neurophysiological responses to design-related stimuli. This study contributes to the extant literature by providing a comprehensive overview of previous EEG studies covering a wide spectrum of design domains.
This paper has three objectives: (i) to review the current research for EEG-based experiments in the field of design; (ii) to analyze the study design according to a population, intervention, control, outcome, and settings (PICOS) framework; and (iii) to discuss the limitations of current research and opportunities in future research.
The systematic review was conducted using the Preferred Reporting Items for Systematic reviews and Meta-Analyses (PRISMA) guidelines by Moher et al. (2009). PRISMA is an evidence-based minimum set of items for reporting in systematic reviews and meta-analyses and it primarily is used to evaluate the effects of interventions (Moher et al., 2009).
The scope of this review includes design research studies of EEG psycho-physiological signals. Table 1 identifies four categories consisting of key terms that reflect our review's objective. The selected keywords ensure the research objectives by adding synonyms and neighboring words. First, studies must involve EEG. Second, studies must adopt a biometric perspective within the analysis. Third, studies must be conducted in the design context; thus, terms identifying the field were grouped. Fourth, the experimental variable was specified to identify the human experience as the research focus. Using reliable databases, we aimed to apply our search terms only to article titles and abstracts. Scopus and Web of Science were utilized as online search databases to gather sources.
Classification of Search Terms
OR | EEG | Biometric | Design | Preference |
Electroencephalogra* | Neurocognition | Interface | Feedback | |
Neurophysiology | Product | Evaluation | ||
Physiological | Interior | Response | ||
Psychological | Architecture | Interaction | ||
Brain | Environment | Conception | ||
Cogniti* | Perform* | |||
Emotion | ||||
Attention |
Table 2 lists the eligibility criteria for the contents. Eligibility criteria for the studies included (a) written in English, (b) published after 2010, (c) available in full-text, and (d) peer-reviewed articles.
Inclusion and Exclusion Criteria
Subject | Human | Non-human (i.e. animals) |
Topic | Design-related | Not related to design (e.g. medicine, mathematics, pharmacology, biochemistry) |
Method | Experimental | Non-experimental (i.e. observational, review, survey or interview) |
Intervention | Design-related | Not related to design No comparators |
Data | Not responding to the PICOS criteria Re-analysis of datasets from previous research Case study of EEG |
The PRISMA flow diagram for the study selection process is depicted in Figure 1. The first identification phase consisted of studies retrieved from online databases via predetermined search strings. The initial search resulted in 10,457 studies from Scopus and 13,223 studies from Web of Science. However, 11,677 studies were excluded before screening due to publication limitations. In the identified articles, 15,678 are duplicates and were removed. In the next phase, inclusion and exclusion criteria in Table 2 were applied to titles and abstracts. A detailed full-text review of 192 articles was conducted to verify the eligibility criteria. Finally, twenty-nine studies were considered eligible for inclusion.
Figure 1 PRISMA Flow Diagram
This review adopted a framework for analysis utilizing the population, intervention, comparator, outcome, settings (PICOS) variation methodology by Higgins and Thomas (2019). Population (P) refers to participant charactersitics such as sample size, age, gender, and condition. Intervention (I) are variables being tested for and Comparator (C ) are conditions of comparison within each group of independent variables. Outcomes (O) are the results of brain data retrieved from the EEG. Setting (S) refers to the controlled experimental conditions in the study.
Participant sample sizes ranged from 8 to 160, with an average of 27. Regarding gender, seven studies did not specify gender; thus, they were excluded. In total, 53.5% of the participants were identified as males and 46.5% as females. The main role of participants was to evaluate the outcomes of participation in the design process and perform design tasks. Participant information is provided in Table 3.
Participants
Al-Samarraieet al. (2019) | 19 | 10 | 9 | age range 20-23 |
Evaluator | Students who have not been to the UK and were unfamiliar with places in London |
Alvino et al. (2021) | 25 | 15 | 10 | 26.4 | Evaluator | Mostly students and employees of the University of Twente |
Aurup andAkgunduz(2012) | 14 | - | - | - | Evaluator | - |
Cao et al. (2021) | 21 | 12 | 9 | 26.7 | Designer | Mechanical engineering postgraduate students at Sichuan University with at least 5 years of engineering experience |
Clemente etal. (2014) | 20 | 6 (1st group) |
4 (1st group) |
age range 22-29 |
Evaluator | 1st group: viewed the environments by a common desktop screen 2nd group: viewed the environment on a power wall screen |
5 (2nd group) |
5 (2nd group) |
age range 21-29 |
||||
Deng andWang (2019) | 20 | - | - | - | Evaluator | Students in the department of industrial design |
Ergan et al. (2019) | 33 | 22 | 11 | age range 21-30 |
Evaluator | Students, faculty, and staff members at a univeristy campus |
Guo et al. (2016) | 14 | 7 | 7 | 25.4 | Evaluator | Students from Northeastern University majoring in management science and engineering with a background of ergonomic |
Guo et al. (2019) | 26 | 16 | 10 | 25.1 | Evaluator | |
Hu and Reid(2018) | 33 | 17 | 16 | 24.3 | Evaluator | All from West Lafayette, Indiana 19: in an engineering program 14: in a non-engineering program |
Khushaba etal. (2013) | 18 | 38 | Evaluator | |||
Kim et al. (2021) | 33 | 0 | 33 | 30s: 26 40s: 7 |
Evaluator | Females who had used private rooms in postpartum care centers |
Li et al. (2017) | 15 | 0 | 15 | Evaluator | Targeted consumer groups for the type of shirts | |
Li et al. (2020) | 30 | 15 | 15 | age range 18-25 |
Evaluator | University students |
Liang et al. (2017) | 12 | 6 | 6 | age range 36-49 |
Designer | i. worked in the design industry in Taiwan for more than 10 years ii. responsible for leading design teams specializing in graphic and multimedia design iii. received awards in international design awards |
Liang et al. (2019) | 24 | 5 (novice) |
7 (novice) |
age range 20-23 |
Designer | i. junior or senior university students majoring in communication or design ii. achieved notable creativity and design performance levels |
6 (expert) |
6 (expert) |
age range 34-45 |
i. worked in the virtual experience industry for more than 10 years ii. being a renowned freelancer or having led design teams iii. received awards in international competition for interaction design | |||
Liu et al. (2014) | 24 | - | - | - | Designer | - |
Liu et al. (2018) | 19 | 13 | 6 | 23.6 | Designer | First-year graduate students from the School of Manufacturing Science and Engineering of Sichuan University |
Llinares et al. (2021) | 160 | 91 | 69 | 23.5 | Evaluator | i. university student ii. have been born and be resident in Spain |
Lou et al. (2017) | 14 | 10 | 4 | 23.5 | Evaluator | Postgraduate or undergraduate students majoring in mechanical engineering at Zhejiang University |
Lou et al. (2020) | 10 | 10 | 0 | 30.6 | Evaluator | From Chinese elevator manufacturing company |
Moon et al. (2019) | 12 (1st session) |
10 (1st session) |
2 (1st session) |
- | Evaluator | 1st session: instructed to stand in front of the car 2nd session: instructed to watch the monitor screen |
4 (2nd session) |
3 (2nd session) |
1 (2nd session) |
||||
Naghibi et al. (2019) | 11 | 5 | 6 | 26 | Evaluator | Graduate students |
Nguyen andZeng (2014) | 11 | age range 25-35 |
Designer | Graduate students from the Quality System Engineering program at Concordia University | ||
Nguyen et al. (2018) | 8 | age range 25-35 |
Designer | Graduate students from the Quality System Engineering program at Concordia University | ||
Shin et al. (2015) | 28 | 16 | 12 | 22.5 | Evaluator | |
Vieira et al. (2020) | 84 | 46 | 38 | 35.5 | Designer | Professional designers (23 mechanical engineers, 23 industrial designers, 27 architects, and 11 graphic designers) |
Yilmaz et al. (2014) | 15 | 5 | 10 | 22 | Evaluator | |
Zhang et al. (2021) | 26 | 12 | 14 | 18-22: 14 23-25: 11 25<: 1 |
Evaluator | Students from Peking University |
Lohmeyer and Meboldt (2016) classified evaluators and designers according to the participants’ roles in the experiment to ensure that corresponding measures and analysis reflect the research objective. In the case of evaluators, the completed visual output was utilized as the stimulus. Whereas in the case of designers, cognitive ability during the design process is the measurable outcome along with their established experience as a comparator. The distinction between evaluators and designers continues to segment the studies' design area (e.g., product, interior, fashion, service, etc.), as shown in Table 4 and Table 5.
Use of EEG in participant type: evaluator
Product | Wine labeling | Alvino et al. (2021) |
Automobile | Aurup and Akgunduz (2012) | |
Bottle (Cultural element) | Deng and Wang (2019) | |
Smartphone form | Guo et al. (2016) | |
LED desk lamp | Guo et al. (2019) | |
Cracker | Khushaba et al. (2013) | |
Elevator | Lou et al. (2020) | |
Automobile | Moon et al. (2019) | |
Shoe | Yilmaz et al. (2014) | |
Interior/ Architecture/ Environmental Design |
Level of luminance Presence of visual cues Presence of natural daylight Color of surfaces Openness |
Ergan et al. (2019) |
Private rooms in postpartum care centers | Kim et al. (2021) | |
Color hue of classroom walls | Llinares et al. (2021) | |
Window shape | Naghibi et al. (2019) | |
Ambient lighting | Shin et al. (2015) | |
Environment (Open natural/Semi-open library/ close basement) |
Li et al. (2020) | |
Urban Street | Zhang et al. (2021) | |
Service | Virtual Environment | Clemente et al. (2014) |
Map | Al-Samarraie et al. (2019) | |
Fashion | Shirts | Li et al. (2017) |
Engineering | Cyber-Physical System (CPS) | Lou et al. (2017) |
Use of EEG in participant type: designer
Design Process | Idea generation | Cao et al. (2021) |
Idea generation | Hu and Reid (2018) | |
Verbalization of visual attention/association | Liang et al. (2017) | |
Verbalization of conceptual imagination | Liang et al. (2019) | |
Configuration and optimization | Liu et al. (2014) | |
Problem-solving | Liu et al. (2018) | |
Problem-solving | Nguyen and Zeng (2014) | |
Problem-solving | Nguyen et al. (2018) | |
Problem-solving and design sketching | Vieira et al. (2020) |
The main path of the design process begins with considering potential users or evaluators. EEG is used in various experimental design research fields to capture evaluators’ responses. In light of this exploration, specific design subjects or elements that lead to evaluators’ appraisal are identified. The studies in each field had unique characteristics.
Product design studies ranged from automobile to daily consuer products. The influence of particular design outputs or specific elements of a particular design factor was assessed based on user preferences or emotions. Most such studies were conducted to determine whether their hypotheses about certain product variations were supported and used visual stimuli such as photographs or prototypes to show these variations. Alvino et al. (2021) elaborated on the influence of extrinsic cues in wine bottle labeling on consumers’ visual attention. They assessed the implications of wine label designs on participants’ brain activity using reaction times and EEG measurement. In consumer neuroscience, biometric measures are expected to provide an improved understanding of users’ purchasing behavior. Guo et al. (2016) discussed the specific elements of smartphone products rather than a complete product. The presented stimuli consisted of colors, screen sizes, edges, and corners of smartphone design. Design studies addressing the influence of specific elements are expected to bridge the gap between evaluators’ purchase behaviors and their unconscious cognition, which may not be addressed in self-rated questionnaires or interviews.
Interior design and architecture studies have mainly examined the relationships between people and spatial design elements, such as lighting, ceiling height, and wall color, on users’ emotional and cognitive responses. Llinares et al. (2021) analyzed the effect of warm and cold hue classroom walls on university students’ attention and cognitive memory function. They carried out an environmental simulation with 24 color configurations on the frontal and lateral walls of a virtualized university classroom. Kim et al. (2021) explored varied architectural elements of private rooms in postpartum care centers and the users’ relaxation-arousal responses to each element were distinguished using the RAB indicator values of EEG.
In service design studies, the effects of user-controlled navigation on the sense of presence were evaluated while demonstrating the usability of the Emotiv EPOC headset (Clemente et al.,2014). Navigation control was tested with two evaluator groups according to screen types and visual stimuli conditions. Al-Samarraie et al. (2019) investigated users’ performance locating a place of interest while utilizing a map. The symbolic and non-symbolic features in users’ cognitive load was presented to determine the effectiveness of map visualization design. Unlike product design studies, service design studies have confirmed the significance of user experience while interacting with the product.
Whereas past design research has focused on participant roles as designers or evaluators, current studies have endeavored to encompass the design process. The design scheme evaluation method proposed by Lou et al. (2020) considers both experts’ evaluation results and customers’ psychological states.
Indicators are concrete research constructs that provide evidence of the condition, behavior, or state (Lohmeyer and Meboldt, 2016). The form of visual stimuli applied to the most studies were photographs, which were used in 7 studies, although the photographs were of different types. For instance, Zhang et al. (2021) selected panoramic photographs of urban street scenes taken by a dual fisheye panoramic camera. They adopted visual pattern metrics to quantify and classify the visual stimuli and analyzed the correlations between three metrics: percentage of landscape (PLAND), landscape division index (DIVISION), and Shannon’s diversity index (SHDI).
Li et al. (2020) analyzed the connection between EEG data and subjective feelings, evaluating peoples’ perceptions of architectural environments by measuring beta waves in the right temporal lobe. They exposed participants to virtual representations of an open, natural, semi-open library, and closed basement spaces while recording EEG data and compared this to participants’ survey responses. Finally, they evaluated the relationship between subjective feelings and beta waves associated with work efficiency and spatial satisfaction.
Intervention and Comparators
Al-Samarraieet al. (2019) | Map design charateristics | 2 types (symbolic/non-symbolic) |
Cartographic feature |
Alvino et al. (2021) | Wine label selected according to hue and color, images, writings, bottle shape, and overall design |
4 wine label designs | Photograph |
Aurup andAkgunduz(2012) | Product feature alternatives | 8 design features of automobiles (2 styles/features/2 colors/ background/aesthetic/style and features) |
Photograph |
Cao et al. (2021) | Design fixation | 2 degrees of fixation (High design fixation level/low design fixation level) |
Design tasks |
Clementeet al. (2014) | Different levels of navigation control (the view of still photograph, the view of a video of an automatic navigation, free navigation through virtual environment) |
3 levels of navigation control (Phtograph/video/navigation) |
Photograph, Video, Virtual Environment |
Screen types | 2 screen types (Common desktop screen/high- resolution power wall screen) |
Screen size | |
Deng andWang(2019) | Picture samples with different emotional states |
6 pictures (cultural elements with different pleasure degree) Design sketch |
Picture |
Ergan et al. (2019) | Level of luminance Presence of visual cues Presence of natural daylight Color of surfaces and openness of spaces |
2 virtual environments (the stress-reducing environment/ the stress-inducing environment) |
Virtual environment |
Guo et al. (2016) | Form features (screen size, color, edges and corners) |
3 pairs (6 pictures in total) | Picture |
Guo et al. (2019) | Visual aesthetic (Morphology, material, color) |
3 visual aesthetic clusters consisted of 32 LED desk lamps (6 lamps of low visual aesthetic/17 lamps of neutral visual aesthetic/9 lamps of high visual aesthetic) |
Virtual prototype (3D) |
Hu and Reid(2018) | Personal context-specific experience | 2 degree of contextual experience (novice designers,/expert designers) |
Design tasks |
Khushabaet al. (2013) | Shape (round, triangle, square), Flavor (wheat, dark rye, plain), Toppings (salt, poppy seed, plain) |
57 choice sets (3 crackers that varied in shape, flavor and toppings) |
Virtual prototype (2D) |
Kim et al. (2021) | Architectural elements (aspect ratio of space, ceiling height, window ratio) |
30 virtual settings | Virtual environment |
Li et al. (2017) | Feature elements | 7 feature elements of women’s shirts (overall/neckline/shoulder/front skirt/ cuff/waist/sweep) |
Product |
Li et al. (2020) | Stroop effect/digital calculation/ meaningless figures recognition/ symbolic digital simulation |
4 types of cognitive experiments | |
Open natural environment, semi-open library environment, closed-basement space |
3 types of scenes | Photograph (Panoramic) |
|
Liang et al. (2017) | Abstractness (Pablo Picasso), Surrealism (Joan Miro), Realism (Jean- François Millet) |
18 Paintings (6 works from each artist) |
Paintings |
Liang et al. (2019) | Realism(Jean-François Millet), Abstractness(Pablo Picasso) |
20 paintings (10 works from each artist) 2 levels of professionality (expert designers/novice designers) |
Paintings |
Liu et al. (2014) | Conventional NX interface, Game- based NX interface |
2 User Interface Attributes | User interface |
Liu et al. (2018) | Design problem statements | 3 design problems (2 engineering design problems and 1 interior design problem) with 3 tasks each (open- ended/decision-making/constrained) |
Design tasks |
Llinares et al. (2021) | Color hue of classroom walls (warm hue, cold hue) |
24 configurations (12 warm, 12 cold) | Virtual environment |
Lou et al. (2017) | Product quality attributes | 3 categories of product quality attributes (must-be/one-dimensional/ attractive) |
Pictures, words |
Lou et al. (2020) | Elevators with alternative design schemes |
3 elevator design schemes | Product |
Moon et al. (2019) | Sedans from different manufacturers (exterior design/interior design/ steering wheel design) |
3 automobiles (3 scenes each) | Product, photograph |
Naghibi et al. (2019) | Window shapes | 16 window shapes (11 windows as pleasant/5 windows as unpleasant) |
Virtual prototype (3D) |
Nguyen andZeng (2014) | Mental effort and mental stress during design problem solving |
Open-ended design problems | Design tasks |
Nguyen et al. (2018) | Design problems of variable difficulty | 7 design problems (3 tasks per problem: sketching problem/multiple choice problem/subjective rating) |
Design tasks |
Shin et al. (2015) | Direct/indirect lighting (400lx downlight, 300lx uplight), direct lighting (700 lx downlight) |
2 physical space | Environment |
Vieira et al. (2022) | Constrained design task based on problem-solving/ Open design task based on design-sketching |
2 types of design task | Design tasks |
Gender | Male and female | ||
Yilmaz et al. (2014) | Shoes with different styles and color | 16 women shoes | Photograph |
Zhang et al. (2021) | Visual patterns of urban streets (element, color, scale) |
39 street scenes (3 spatial scales: 13 small/13 medium/13 large) |
Photograph (Panoramic) |
Preferences are commonly measured to evaluate products and services. Significant changes in alpha waves can be observed in the frontal, central, occipital, and left temporal lobes in the Brodmann area. For example, Guo et al. (2019) asked participants to look at virtual lamp prototypes. They found that preference for lamps was positively correlated with alpha power, as detected by EEG in the frontal, central, parietal, occipital, left temporal, and right temporal regions of the brain. Table 7 summarizes intervention variables, comparators and stimuli.
Outcome
Al-Samarraieet al. (2019) | Emotiv EPOC | MATLAB | ERD (Event-Related Desynchronization) of alpha, theta and beta bands |
Performance, Cognitive states |
ICA, MARA (Multiple Artefact Rejection Algorithm) |
Alvinoet al. (2021) | EasyCap-62 channel cap, ActiChamp amplifier |
BrainVision Recorder, BrainVision Analyzer |
PCN (Posterior Contralateral Negativity) amplitude |
Preference | ICA, ANOVA |
Aurup andAkgunduz(2012) | ProComp2 | BioGraph Infinity, EEG Suite, Minitab |
Alpha peak frequency (F3 and F4) |
Preference | Statistical (linear-trend line analysis) |
Cao et al. (2021) | actiChamp-32 Research Amplifier |
BrainVision Recorder, BrainVision Analyzer |
Alpha band TRP (task-related power) changes in frontal, parietotemporal, occipital, and centroparietal |
Fixation | MANOVA, Kruskal- Wallis ANOVA |
Clementeet al. (2014) | Emotiv EPOC | EEGLAB | The innsula for the alpha and theta bands |
Presence | SPSS, sLORETA, voxel-wise t-tests |
Deng andWang(2019) | EEG equipment (German Brain Products) |
Brain Vision Recorder, Brain Vision Analyzer |
Frontal lobe, the power of frontal alpha wave |
Preference, Emotion |
IGA, BPNN |
Erganet al. (2019) | 14 channel EEG headset (Not further specified) |
NR | Alpha, theta, beta oscillations across frontal channels |
Stress, Anxiety |
Power spectrum |
Guoet al. (2016) | Neuroscan | Curry 7.0 SBA | N2, P2, and P3 | Preference | ANOVA |
Guoet al. (2019) | Neuroscan | Curry 7.0 SBA, MATLAB, E-Prime |
Alpha and gamma power | Appreciation | ANOVA |
Hu andReid(2018) | B-Alert X10 headset |
iMotions, Minitab |
Alpha wave channel activation on F2, F3, F4, Cz, C3, C4, POz, P3, and P4 |
Performance, Cognitive states |
ANOVA |
Khushabaet al. (2013) | Emotiv EPOC | Emotiv Software Development Kit (SDK), MATLAB |
Alpha, beta and delta across the frontal (F3, F4, FC5 and FC6), temporal (T7), and occipital (O1) |
Preference | ICA, DWT, power spectrum |
Kimet al. (2021) | EEG DSI-24 | SWDS- Istreamer, TeleScan |
Alpha and beta wave frequencies, RAB (alpha/beta ratios) in the prefrontal (Fp1 and Fp2), frontal (F3 and F4), parietal (P3 and P4), and occipital lobes (O1 and O2) |
Relaxation- arousal response |
Wilcox signed- rank test |
Li et al. (2017) | Emotiv EPOC | MATLAB | Not specified | Preference | ICA, DWT, descriptive statistics |
Li et al. (2020) | EEG signal acquisition cap (Not further specified) |
NR | Right temporal lobe, beta rhythm | Satisfaction, Work efficiency |
Statistical regression, correlations |
Lianget al. (2017) | Brain Rhythm EEG headset |
EEGLAB | Frontoparietal, prefrontal, frontocentral, parietoocipital regions Beta power in channels Cz, F4, F8, Fz, FCz, F7, and FC3, Alpha power in channels Cz, F4, F8, Fz, FCz, F7, and FC3, Gamma power in channels, Cz, Pz, O1, FCz, C4, FT8, FC3, and FT7 |
Attention, Association |
ANOVA, ICA |
Lianget al. (2019) | EEG cap BR32S headset |
EEGLAB | The right prefrontal in all frequency bands, the left temporal cluster, the right temporal cluster, the delta, theta, slow alpha, middle beta, high beta, and low gamma bands |
Imagination | ICA |
Liu et al. (2014) | NeXus-32 | EEGLAB | Alpha peak frequency of the frontal lobe |
Emotion | Fuzzy model, ICA, IIR |
Liu et al. (2018) | actiChamp-32 | BrainVision Analyzer |
The alpha band in the frontal, parietotemporal, occipital regions of the right hemisphere, the theta and beta bands in the centrotemporal regions of the left hemisphere, the activations in the centroparietal and parietoocipital regions |
Cognitive behavior |
Descriptive statistics, ANOVA, ANCOVA |
Llinareset al. (2021) | b-Alert x10 | EEGLAB | The beta band (C3, CZ), the high beta band (F3, FZ) |
Cognitive behavior (Attention and memory) |
ANOVA, correlations, the Mann Whitney test |
Louet al. (2017) | EEG cap with 32 electrodes (Not further specified) |
Neuroscan Nuamps amplifier, LIBSVM |
Sample entropy | Needs | SVM (Support Vector Machine) |
Louet al. (2020) | EEG cap with 32 electrodes (Not further specified) |
Neuroscan Nuamps amplifier, Curry 7.0 |
Sample entropy | Emotion | Cloud model, TOPSIS, ILPGWA |
Moonet al. (2019) | Emotiv EPOC+ | LIBSVM | PSD (Power Spectral Density) | Preference | ANOVA, ICA, correlations |
Naghibiet al. (2019) | ANT Neuro ASA- Lab 64+8 ES |
MATLAB, R software 3.4.2 |
The peaks of P3 and N1 in parietal and occipital channels, the peak of P2 in the frontal and central lobes |
Emotion | Wilcoxon signed- rank, rank sum test, descriptive statistics |
NguyenandZeng(2014) | Grass 15LT | In-house software system, MATLAB |
Fpz, Fz, F4, F3, C4, C3, T4, T3, P4, P3, T6, T5, O2, and O1 |
Stress | Statistical tests |
Nguyenet al. (2018) | NR | NR | Transient microstate percentage, alpha range, beta range, theta range, delta range, (theta+alpha)/ beta, alpha/beta, (theta+alpha)/ (alpha+beta), theta/beta |
Effort, Fatigue, Concentration |
RBF (Radial Basis Function) interpolatio, microstate clustering, correlations |
Shinet al. (2015) | Quik-cap, NuAMP amplifier |
MATLAB | Theta oscillations on the F4, F8, T4, and TP7 |
Emotion | paired t-tests |
Vieiraet al. (2022) | Emotiv EPOC+ | MATLAB | Theta, alpha, and beta bands | Gender | Statistical tests |
Yilmazet al. (2014) | EEG 1200 | MATLAB, in- house software |
4Hz and 5Hz in the low frequency band, frontal channel on the left (F7-A1), temporal channel on the right (T6-A2), central (Cz-A1), occipital (O1-A1) |
Preference | Statistical regression |
Zhanget al. (2021) | Emotiv EPOC+ | MATLAB | Utilization of EMOTIV performance metrics algorithms for cognitive states (Not further specified) |
Emotion | Statistical tests |
To study the role of biometric technology, in this case, EEG technology, the focus should be on the instruments and equipment employed throughout the experimental practice (Radder, 2009). From this perspective, Table 7 illustrates EEG hardware and software tools utilized in experiments. Five studies did not report the name of either the hardware or the software. Emotiv EPOC/EPOC+ is the most widely used EEG hardware, and MATLAB is the most commonly used software. Meanwhile, more than one software tool was utilized in 14 studies.
Past research has increasingly considered the relationship between psychological measures, theory, and design research methodology. Nguyen et al. (2018) highlighted the ongoing conceptual design process by focusing on the aspects of effort, fatigue, and concentration.
While concentrating on the design process of constrained and open design tasks, Vieira et al. (2022) discussed the effect of gender on EEG frequency bands. Gender was also included as a control variable in Zhang et al. (2021). Gender turned out to have a significant effect on the physiological indicators, but not on the subjective evaluations.
To evaluate perceptual responses to product design, Moon et al. (2019) used EEG and eye-tracking to strengthen the viability of the experiment. The study’s finding demonstrated that perception of car design can be predicted via implicit monitoring based on EEG and gaze data (Moon et al., 2019).
Additional elicitation methods (i.e., survey, interview, video analysis, etc.) were employed in several studies to compare EEG signals with subjective evaluations and to identify biosignal indicators. Combining physiological and traditional methods (i.e., EEG and other subjective evaluation methods) is a preferred approach that can elucidate elusive dimensions of the human experience. Twelve studies applied different types of questionnaires. Nguyen et al. (2018) used NASA-TLX for the subjects to rate their workload. Lou et al. (2017) adopted Kano’s questionnaire (Kano et al., 1984) that included functional and dysfunctional questions to explore psychological states to identify the achievement of a specific quality attribute. Zhang et al. (2021) pointed out the need for interviews or questionnaire since they found negative correlations between four out of six EEG indicators, even though the official algorithms of the Emotiv emotional indicator were adopted. Kim et al. (2021) found some similarities between EEG response and questionnaire results, based on which they suggested integrating self-reported assessments with EEG to further identify the relationship between psychological and physiological measurements. The reviewed studies primarily used questionnaires to verify the relationship between EEG signals and participants’ subjective ratings.
As each biometric measurement is related to particular aspects of the human body, a deliberate application of various biometric measures supported by corresponding knowledge may support empirical data (Lohmeyer and Meboldt, 2016). Table 8 summarizes biosignals adopted by each study. For example, Lou et al. (2017) used EEG in the analysis, whereas the recorded EOG was only used to reject the artifacts. Eye-tracking and heart rate were the most utilized biosignals, along with EEG. Moon et al. (2021) included both EEG and eye-tracking signals to demonstrate the affective user experience of car designs. The eye-tracking analysis supported conclusions for two independent variables. Vieira et al. (2022) transformed fMRI tasks described in Alexiou et al. (2009) into EEG problem-solving tasks. Recent EEG studies have increasingly incorporated different biometric measures and adopted multimodal experimental tasks measured by other biosignals such as ECG, EDA, and heart rate.
Biometric Measures
We found three different experimental environments in the EEG experiments: laboratory, field, and virtual. Correlation studies between EEG and human experience have been conducted predominantly in the laboratory, as the real world contains a wide range of external stimuli that may affect measurement. Kim et al. (2020) conducted an EEG experiment in a real-world environment and classified it as a field experiment. While the definition of field experiments may vary, the classification of field experiments and laboratory experiments remains elusive. Experiments with neuronal activity measures during controlled tasks can be considered field experiments since brain functioning is presumed to be a natural reaction to the controlled stimuli (Harrison and List, 2004). In this review, we defined a field experiment as a direct interaction between real-world products in an uncontrolled environment, which may include external factors of the surrounding. Lou et al. (2020) used a150 m high elevator test tower in the experiment. The participants took three different elevators with varying design schemes, and EEG data were measured while taking the elevator, which allowed us to classify it as a field experiment. In the case of Moon et al. (2021), the first session of the experiment was conducted in front of a car. Moreover, Moon et al. (2021) tried to verify whether a photograph can substitute for real products in two experimental sessions in which they compared perceptual responses induced by the photograph of a car and a real car.
Settings
Al-Samarraie et al. (2019) | Laboratory | Monitor |
Alvino et al. (2021) | Laboratory | Computer screen (24-inch AOC G2460P LED computer) |
Aurup and Akgunduz (2012) | Laboratory | 17-inch monitor |
Cao et al. (2021) | Laboratory | NR |
Clemente et al. (2014) | Laboratory | Common desktop screen/Power wall screen |
Deng and Wang (2019) | Laboratory | Computer screen (Presented by E-prime software) |
Ergan et al. (2019) | Virtual | 98-inch touch screen |
Guo et al. (2016) | Laboratory | Computer screen (presented by E-Prime software) |
Guo et al. (2019) | Laboratory | Monitor |
Hu and Reid (2018) | Laboratory | Computer screen |
Khushaba et al. (2013) | Laboratory | Computer screen |
Kim et al. (2021) | Virtual | VR headset (HTC Vive) |
Li et al. (2017) | Laboratory | NR |
Li et al. (2020) | Virtual | VR integrated helmet (at a semi-circular dome experimental cabin with a radius of 2.4m) |
Liang et al. (2017) | Laboratory | Slide show of prerecorded visual stimuli |
Liang et al. (2019) | Laboratory | Computer screen |
Liu et al. (2014) | Laboratory | NR |
Liu et al. (2018) | Laboratory | Computer screen |
Llinares et al. (2021) | Virtual | VR headset (HTC Vive) |
Lou et al. (2017) | Laboratory | Computer screen |
Lou et al. (2020) | Field | Elevators |
Moon et al. (2019) | 1st session: Field 2nd session: Laboratory |
1st sesssion: Standing in front of the car 2nd session: Display on 84-inch LCD monitor |
Naghibi et al. (2019) | Laboratory | S221HQLBD 21.5 inch LCD monitor |
Nguyen and Zeng (2014) | Laboratory | Tablet |
Nguyen et al. (2018) | Laboratory | Touchpad |
Shin et al. (2015) | Laboratory | Experimental Room (4.7m x 4.4m x 3.1m) |
Vieira et al. (2022) | Laboratory | Mauraria Creative Hub (University of Porto) |
Yilmaz et al. (2014) | Laboratory | Fixed laptop computer (15-inch monitor) |
Zhang et al. (2021) | Virtual | VR headset (HTC Vive) |
EEG has been used in various experimental design research fields, such as product, service, fashion, architecture, and engineering, to explore how participants react to specific design elements. However, studies in each field had unique characteristics and EEG measurements.
Architecture studies mainly examined the relationships of people and environmental elements, such as ceiling height, lighting, and wall color, with users’emotional and cognitive responses. This paper suggests identifying design elements, such as layout, furniture, and material that affect users' experiences of built and virtual environments. Product and packaging design studies examined user preferences for designs. Most such studies were conducted to explore certain product variations using photos showing these variations as stimuli. These studies’ results can be used to create designs that cause certain emotional responses among consumers. Consumer marketing studies have examined the effects of visual marketing on relaxation, attention, and emotion utilizing EEG. They compared design elements, such as arrangement, colors, structures, and shapes of marketing features. Future research should explore how specific marketing techniques affect emotion and attention and whether these effects differ by delivery platform.
Experimental design studies based on EEG data are currently largely focused on evaluating how stimuli affect people’s decision-making, opinions, and emotional responses. Most studies were conducted with participants outside the relevant research field to secure more representative data. However, limited EEG-based research has been conducted on designers’ thought processes. In experimental design studies that feature EEG, participants complete tasks while their EEG signals are being recorded. Future research should be conducted to better understand designers’ creative thought processes.
In data analysis methods, EEG data in current studies are limited to analysis using statistical tools. Future studies can be extended to develop classification and perdition models using machine learning algorithms to forecast individuals’ cognitive, emotional, and behavioral patterns and preferences. Furthermore, it would be meaningful to further explore and discuss the relationship between anatomical activities such as Brodmann areas and cognitive effects during the thinking process. Future studies can explore the correlation between electrode placement across prefrontal, frontal, parietal, occipital lobe and correlate results with the Brodmann area and further interpret the meaning of differences in cognitive terms. Most current studies are limited to the use of EEG, which might affect the generalizability of the results. Further studies could combine EEG experiments with multimodal biometric tools, such as fMIR, ECG, EMG, GSR, and eye-tracking, as each tool is limited to measuring different human factors. This strategy can provide more comprehensive results. The mixed use of subjective interviews and surveys and objective methodology using biometrics can allow for cross-validation and clarification of results.
Our findings revealed a lack of investigations on how EEG can be used in design research on problem formulation or teams' perspectives. Rather than analyzing how perceptions of design are related to certain neural pathways, EEG channels, or Brodmann areas, most design studies have largely examined whether they could observe changes in brain signals to assess participants’ preferences, stress, and simple emotional responses. Thus, design studies should use the EEG’s capabilities more fully to investigate a continuous design process, rather than being limited to just evaluation and problem-solving. The analysis demonstrated the need to recognize design as a dynamic phenomenon and consider broader aspects of design research to integrate the multi-levels of design using EEG.
Experimental design studies on EEG channel indicators are limited. Even though each of the studies included in this study examined EEG channels, activated brain areas, and their related indicators, they did not produce consistent results about which behaviors were correlated with the activation of given brain areas due to the complexity of human cognition. Additionally, even though the studies were selected using a structured procedure, our decision to include a given paper was ultimately subjective. Non-design studies were excluded because we focused on how EEG is applied specifically in this field, which resulted in a biased sample.
Design research has developed as a multidisciplinary field. It incorporates biometric measures to gain further insights into human activities. Dinar et al. (2016) provided a systems-level view of the design process, encompassing major aspects of the design process. The cognitive process involved in the design activities align with the following levels.
Previously, the 29 identified EEG studies were classified based on the participant type of the experiment. In this respect, most studies were categorized into user, artifact, process, and designer levels. For instance, Guo et al. (2019) quantified the visual aesthetics of a LED desk lamp. The visual representation of the design was explored with the appreciation flow of evaluators (i.e., users). The elevator experiment of Lou et al. (2020) accounted for the ergonomics of artifacts. Navigation control was measured by Al-Samarrie et al. (2019) to illustrate the simulation and optimization model of the artifact, in this case, a map, which encompasses the design process.
Since the development of design research, many empirical studies have been conducted on virtually all aspects of design. However, EEG-based design studies have not explored to investigate multi-level perspectives (i.e., problem formulation, designer and teams, process, artifacts, and user) based on the framework of design (Dinar et al., 2016). Current studies are limited to evaluating artifacts or the design process. They have rarely identified problem formulation or group dynamics. Problem formulation or problem framing sheds light on the fact that designers are not limited to solving given problems, but need to further find and develop problems themselves (Cross, 2001). EEG has hardly been considered when trying to explore undetected problems. According to Dinar et al. (2016), the design field investigates teams to ensure their effectiveness (i.e., teamology) and interaction between members (i.e., group dynamics). As design is dynamic and intergrated, broader aspects must be considered in design research using EEG to incorporate the multi-level design.
In light of current developments regarding the intersection of design and neuroscience, this study conducted a systematic review of design studies using neurophysiological measures, especially EEG, to explore the responses of evaluators and designers. This study provides information about how EEG is used in experimental design research, shedding light on the application of neuroscience methodologies in various design fields, including but not limited to product design, interior (or architecture) design, and service design. The review indicated that neuroscience techniques can be used to collect biometric data for design research. Although biometric tools for neurocognition and neurophysiology have not yet been widely applied in design studies, further research should utilize such tools to understand design from an academic perspective. Currently, EEG research has focused mainly on determining how EEG can be used by examining the relationship between EEG data and data collected using traditional methods, or it focused on hypothesis testing. In both cases, EEG-specific experiments can provide evidence-based design.
In design research, EEG was widely used to study designers’ cognitive and affective states (Zhao et al., 2020). EEG is used to measure neurophysiological activation while designing and problem-solving (Vieira et al., 2020). However, EEG studies to understand users’ brain responses and design neurocognition to architectural environments are still at an early stage. Using advanced biosensing technology researchers can look at not only the built environment, but also investigate neurophysiological, physiological, and psychological responses in a virtual environment (Kim and Lee 2021; Mostafavi, 2021; Bower et al., 2019). Combining VR, EEG and body sensors has the potential to quantify human experience (Kim and Kim, 2022; Borgianni and Maccioni, 2020; Ergan et al., 2019). The implications of the findings can help architects and designers consider the effects of design elements to optimize user experience.
Future studies should be conducted with a wider definition of “design” as either a noun, which refers to creation of an entity, or as a verb, which refers to process or a series of activities (Miller, 2004). It is necessary to better understand the use of EEG in design research and its direct and indirect effects. Future studies can investigate the dynamic aspects of the design process and decision making. EEG-based design experimentation will offer more evidence-based insights in design research and practice.
This work was supported by Basic Science Research Program through the National Research Foundation of Korea(NRF) funded by the Ministry of Education (NRF - 2020R1I1A1A01073447).
- 1 . Akash, K., Hu, W. L., Jain, N., & Reid, T. (2018). A classification model for sensing human trust in machines using EEG and GSR. ACM Transactions on Interactive Intelligent Systems (TiiS), 8(4), 1-20. [https://doi.org/10.1145/3132743]
- 2 . Alexiou, K., Zamenopoulos, T., Johnson, J. H., & Gilbert, S. J. (2009). Exploring the neurological basis of design cognition using brain imaging: some preliminary results. Design Studies, 30(6), 623-647. [https://doi.org/10.1016/j.destud.2009.05.002]
- 3 . Al-Samarraie, H., Eldenfria, A., Price, M. L., Zaqout, F., & Fauzy, W. M. (2019). Effects of map design characteristics on users' search performance and cognitive load: An empirical study. The Electronic Library. [https://doi.org/10.1108/EL-10-2018-0202]
- 4 . Alvino, L., Constantinides, E., & van der Lubbe, R. H. (2021). Consumer Neuroscience: Attentional Preferences for Wine Labeling Reflected in the Posterior Contralateral Negativity. Frontiers in psychology, 4490. [https://doi.org/10.3389/fpsyg.2021.688713]
- 5 . Aurup, G. M., & Akgunduz, A. (2012). Pair-wise preference comparisons using alpha-peak frequencies. Journal of Integrated Design and Process Science, 16(4), 3-18. [https://doi.org/10.3233/jid-2012-0021]
- 6 . Bakker, J., Pechenizkiy, M., & Sidorova, N. (2011, December). What's your current stress level? Detection of stress patterns from GSR sensor data. In 2011 IEEE 11th international conference on data mining workshops (pp. 573-580). IEEE. [https://doi.org/10.1109/ICDMW.2011.178]
- 7 . Ball, L. J., & Christensen, B. T. (2019). Advancing an understanding of design cognition and design metacognition: Progress and prospects. Design Studies, 65, 35-59. [https://doi.org/10.1016/j.destud.2019.10.003]
- 8 . Borgianni, Y., & Maccioni, L. (2020). Review of the use of neurophysiological and biometric measures in experimental design research. AI EDAM, 34(2), 248-285. [https://doi.org/10.1017/S0890060420000062]
- 9 . Born, J., Ramachandran, B. R. N., Pinto, S. A. R., Winkler, S., & Ratnam, R. (2019). Multimodal Study of the Effects of Varying Task Load Utilizing EEG, GSR and Eye-Tracking. bioRxiv, 798496. [https://doi.org/10.1101/798496]
- 10 . Bower, I., Tucker, R., & Enticott, P. G. (2019). Impact of built environment design on emotion measured via neurophysiological correlates and subjective indicators: A systematic review. Journal of environmental psychology, 66, 101344. [https://doi.org/10.1016/j.jenvp.2019.101344]
- 11 . Bradley, M. M., Miccoli, L., Escrig, M. A., & Lang, P. J. (2008). The pupil as a measure of emotional arousal and autonomic activation. Psychophysiology, 45(4), 602-607. [https://doi.org/10.1111/j.1469-8986.2008.00654.x]
- 12 . Camp, M. V., Boeck, M. D., Verwulgen, S., & Bruyne, G. D. (2018, July). EEG technology for UX evaluation: a multisensory perspective. In International Conference on Applied Human Factors and Ergonomics (pp. 337-343). Springer, Cham. [https://doi.org/10.1007/978-3-319-94866-9_34]
- 13 . Cao, J., Zhao, W., & Guo, X. (2021). Utilizing EEG to Explore Design Fixation during Creative Idea Generation. Computational Intelligence and Neuroscience, 2021. [https://doi.org/10.1155/2021/6619598]
- 14 . Clemente, M., Rodríguez, A., Rey, B., & Alcañiz, M. (2014). Assessment of the influence of navigation control and screen size on the sense of presence in virtual reality using EEG. Expert Systems with Applications, 41(4), 1584-1592. [https://doi.org/10.1016/j.eswa.2013.08.055]
- 15 . Chrysikou, E., & Gero, J. S. (2020) Using neuroscience techniques to understand and improve design cognition. AIMS Neuroscience, 7(3), 319-326. [https://doi.org/10.3934/Neuroscience.2020018]
- 16 . Chua, H. F., Boland, J. E., & Nisbett, R. E. (2005). Cultural variation in eye movements during scene perception. Proceedings of the National Academy of Sciences, 102(35), 12629-12633. [https://doi.org/10.1073/pnas.0506162102]
- 17 . Conte, S., Casciaro, F., Wang, F., Altamura, M., Bellomo, A., Serafini, G., ... & Conte, E. (2018). Measurements of Electroencephalogram (EEG), Galvanic Skin Resistance (GSR) and Heart Rate Variability (HRV) during the Application of a System that Gives Simultaneously tVNS and Brain Entrainment on Subjects Affected by Depression and Anxiety. Ann Depress Anxiety, 5(2), 1095. [https://doi.org/10.26420/anndepressanxiety.1095.2018]
- 18 . Cross, N. (2001). Design cognition: Results from protocol and other empirical studies of design activity. Design knowing and learning: Cognition in design education, 79-103. [https://doi.org/10.1016/B978-008043868-9/50005-X]
- 19 . Damasio, A., & Carvalho, G. B. (2013). The nature of feelings: evolutionary and neurobiological origins. Nature reviews neuroscience, 14(2), 143-152. [https://doi.org/10.1038/nrn3403]
- 20 . Deng, L., & Wang, G. (2019). Application of EEG and interactive evolutionary design method in cultural and creative product design. Computational intelligence and neuroscience, 2019. [https://doi.org/10.1155/2019/1860921]
- 21 . Dinar, M., Summers, J. D., Shah, J., & Park, Y. S. (2016). Evaluation of empirical design studies and metrics. In Experimental design research (pp. 13-39). Springer, Cham. [https://doi.org/10.1007/978-3-319-33781-4_2]
- 22 . Ergan, S., Radwan, A., Zou, Z., Tseng, H. A., & Han, X. (2019). Quantifying human experience in architectural spaces with integrated virtual reality and body sensor networks. Journal of Computing in Civil Engineering, 33(2), 04018062. [https://doi.org/10.1061/(ASCE)CP.1943-5487.0000812]
- 23 . Fadeev, K. A., Smirnov, A. S., Zhigalova, O. P., Bazhina, P. S., Tumialis, A. V., & Golokhvast, K. S. (2020). Too real to be virtual: Autonomic and EEG responses to extreme stress scenarios in virtual reality. Behavioural neurology, 2020. [https://doi.org/10.1155/2020/5758038]
- 24 . Gero, J. S., & Milovanovic, J. (2020). A framework for studying design thinking through measuring designers’ minds, bodies and brains. Design Science, 6. [https://doi.org/10.1017/dsj.2020.15]
- 25 . Guo, F., Ding, Y., Wang, T., Liu, W., & Jin, H. (2016). Applying event related potentials to evaluate user preferences toward smartphone form design. International Journal of Industrial Ergonomics, 54, 57-64. [https://doi.org/10.1016/j.ergon.2016.04.006]
- 26 . Guo, F., Li, M., Hu, M., Li, F., & Lin, B. (2019). Distinguishing and quantifying the visual aesthetics of a product: an integrated approach of eye-tracking and EEG. International Journal of Industrial Ergonomics, 71, 47-56. [https://doi.org/10.1016/j.ergon.2019.02.006]
- 27 . Heo, J., & Chung, K. (2019). EEG recording method for quantitative analysis. Korean Journal of Clinical Laboratory Science, 51(4), 397-405. [https://doi.org/10.15324/kjcls.2019.51.4.397]
- 28 . Heo, J., & Yoon, G. (2020). EEG studies on physical discomforts induced by virtual reality gaming. Journal of Electrical Engineering & Technology, 15(3), 1323-1329. [https://doi.org/10.1007/s42835-020-00373-1]
- 29 . Harrison, G. W., & List, J. A. (2004). Field experiments. Journal of Economic literature, 42(4), 1009-1055. [https://doi.org/10.1257/0022051043004577]
- 30 . Higgins, J. P., Thomas, J., Chandler, J., Cumpston, M., Li, T., Page, M. J., & Welch, V. A. (Eds.). (2019). Cochrane handbook for systematic reviews of interventions. John Wiley & Sons. [https://doi.org/10.1002/9781119536604]
- 31 . Hu, W. L., & Reid, T. (2018). The Effects of Designers' Contextual Experience on the Ideation Process and Design Outcomes. Journal of Mechanical Design, 140(10). [https://doi.org/10.1115/1.4040625]
- 32 . Hu, L., & Shepley, M. M. (2022). Design Meets Neuroscience: A Preliminary Review of Design Research Using Neuroscience Tools. Journal of Interior Design, 47(1), 31-50. [https://doi.org/10.1111/joid.12213]
- 33 . Jankowiak, K., & Korpal, P. (2018). On modality effects in bilingual emotional language processing: Evidence from galvanic skin response. Journal of Psycholinguistic Research, 47(3), 663-677. [https://doi.org/10.1007/s10936-017-9552-5]
- 34 . Jaiswal, N., Ray, W., & Slobounov, S. (2010). Encoding of visual-spatial in- formation in working memory requires more cerebral efforts than retrieval: Evidence from an EEG and virtual reality study. Brain research, 1347, 80-89. [https://doi.org/10.1016/j.brainres.2010.05.086]
- 35 . Hu, W. L., & Reid, T. (2018). The effects of designers' contextual experience on the ideation process and design outcomes. Journal of Mechanical Design, 140(10). [https://doi.org/10.1115/1.4040625]
- 36 . Kang, D., Kim, J., Jang, D. P., Cho, Y. S., & Kim, S. P. (2015). Investigation of engagement of viewers in movie trailers using electroencephalography. Brain-Computer Interfaces, 2(4), 193-201. [https://doi.org/10.1080/2326263X.2015.1103591]
- 37 . Kano, N. (1984). Attractive quality and must-be quality. Hinshitsu (Quality, The Journal of Japanese Society for Quality Control), 14, 39-48.
- 38 . Khushaba, R. N., Wise, C., Kodagoda, S., Louviere, J., Kahn, B. E., & Townsend, C. (2013). Consumer neuroscience: Assessing the brain response to marketing stimuli using electroencephalogram (EEG) and eye tracking. Expert systems with applications, 40(9), 3803-3812. [https://doi.org/10.1016/j.eswa.2012.12.095]
- 39 . Kim, J., & Kim, N. (2022). Quantifying Emotions in Architectural Environments Using Biometrics. Applied Sciences, 12(19), 9998. [https://doi.org/10.3390/app12199998]
- 40 . Kim, N., & Lee, H. (2021). Assessing Consumer Attention and Arousal Using Eye-Tracking Technology in Virtual Retail Environment. Frontiers in Psychology, 2861. [https://doi.org/10.3389/fpsyg.2021.665658]
- 41 . Kim, M., Chong, S. C., Chun, C., & Choi, Y. (2017). Effect of thermal sensation on emotional responses as measured through brain waves. Building and Environment, 118, 32-39. [https://doi.org/10.1016/j.buildenv.2017.03.023]
- 42 . Kim, Y., Han, J., & Chun, C. (2020). Evaluation of comfort in subway stations via electroencephalography measurements in field experiments. Building and Environment, 183, 107130. [https://doi.org/10.1016/j.buildenv.2020.107130]
- 43 . Kim, S., Park, H., & Choo, S. (2021). Effects of Changes to Architectural Elements on Human Relaxation-Arousal Responses: Based on VR and EEG. International Journal of Environmental Research and Public Health, 18(8), 4305. [https://doi.org/10.3390/ijerph18084305]
- 44 . Kirk, U., Skov, M., Christensen, M. S., & Nygaard, N. (2009). Brain correlates of aesthetic expertise: a parametric fMRI study. Brain and cognition, 69(2), 306-315. [https://doi.org/10.1016/j.bandc.2008.08.004]
- 45 . Kober, S. E., & Neuper, C. (2011). Sex differences in human EEG theta oscillations during spatial navigation in virtual reality. International Journal of Psychophysiology, 79(3), 347-355. [https://doi.org/10.1016/j.ijpsycho.2010.12.002]
- 46 . Lee, Y. Y., & Hsieh, S. (2014). Classifying different emotional states by means of EEG-based functional connectivity patterns. PloS one, 9(4), e95415. [https://doi.org/10.1371/journal.pone.0095415]
- 47 . Li, B. R., Wang, Y., & Wang, K. S. (2017). A novel method for the evaluation of fashion product design based on data mining. Advances in Manufacturing, 5(4), 370-376. [https://doi.org/10.1007/s40436-017-0201-x]
- 48 . Li, J., Jin, Y., Lu, S., Wu, W., & Wang, P. (2020). Building environment information and human perceptual feedback collected through a combined virtual reality (VR) and electroencephalogram (EEG) method. Energy and Buildings, 224, 110259. [https://doi.org/10.1016/j.enbuild.2020.110259]
- 49 . Liang, C., Chang, C. C., & Liu, Y. C. (2019). Comparison of the cerebral activities exhibited by expert and novice visual communication designers during idea incubation. International Journal of Design Creativity and Innovation, 7(4), 213-236. [https://doi.org/10.1080/21650349.2018.1562995]
- 50 . Liang, C., Lin, C. T., Yao, S. N., Chang, W. S., Liu, Y. C., & Chen, S. A. (2017). Visual attention and association: An electroencephalography study in expert designers. Design Studies, 48, 76-95. [https://doi.org/10.1016/j.destud.2016.11.002]
- 51 . Lim, J. S., Whang, M. C., Park, H. K., & Lee, H. S. (1998). A physiological approach to the effect of emotion on time series judgmental forcecasting EEG and GSR. Science of Emotion and Sensibility, 1(1), 123-133.
- 52 . Liu, L., Li, Y., Xiong, Y., Cao, J., & Yuan, P. (2018). An EEG study of the relationship between design problem statements and cognitive behaviors during conceptual design. AI EDAM, 32(3), 351-362. [https://doi.org/10.1017/S0890060417000683]
- 53 . Liu, Y., Ritchie, J. M., Lim, T., Kosmadoudi, Z., Sivanathan, A., & Sung, R. C. (2014). A fuzzy psycho-physiological approach to enable the understanding of an engineer's affect status during CAD activities. Computer-Aided Design, 54, 19-38. [https://doi.org/10.1016/j.cad.2013.10.007]
- 54 . Llinares, C., Higuera-Trujillo, J. L., & Serra, J. (2021). Cold and warm coloured classrooms. Effects on students' attention and memory measured through psychological and neurophysiological responses. Building and Environment, 196, 107726. [https://doi.org/10.1016/j.buildenv.2021.107726]
- 55 . Lohmeyer, Q., & Meboldt, M. (2016). The integration of quantitative biometric measures and experimental design research. In Experimental design research (pp. 97-112). Springer, Cham. [https://doi.org/10.1007/978-3-319-33781-4_6]
- 56 . Lou, S., Feng, Y., Li, Z., Zheng, H., & Tan, J. (2020). An integrated decision-making method for product design scheme evaluation based on cloud model and EEG data. Advanced Engineering Informatics, 43, 101028. [https://doi.org/10.1016/j.aei.2019.101028]
- 57 . Lou, S., Feng, Y., Tian, G., Lv, Z., Li, Z., & Tan, J. (2017). A cyber-physical system for product conceptual design based on an intelligent psycho-physiological approach. IEEE Access, 5, 5378-5387. [https://doi.org/10.1109/ACCESS.2017.2686986]
- 58 . Ma, Q. G., Shang, Q., Fu, H. J., & Chen, F. Z. (2012). Mental workload analysis during the production process: EEG and GSR activity. In Applied Mechanics and Materials (Vol. 220, pp. 193-197). Trans Tech Publications Ltd. [https://doi.org/10.4028/www.scientific.net/AMM.220-223.193]
- 59 . Merla, A., & Romani, G. L. (2007, August). Thermal signatures of emotional arousal: a functional infrared imaging study. In 2007 29th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (pp. 247-249). IEEE. [https://doi.org/10.1109/IEMBS.2007.4352270]
- 60 . Miller, W. R. (2004). Definition of design. Environmental Systems Research Institute Redlands, California.
- 61 . Moher, D., Liberati, A., Tetzlaff, J., Altman, D. G., & PRISMA Group*. (2009). Preferred reporting items for systematic reviews and meta-analyses: the PRISMA statement. Annals of internal medicine, 151(4), 264-269. [https://doi.org/10.7326/0003-4819-151-4-200908180-00135]
- 62 . Moon, S. E., Kim, J. H., Kim, S. W., & Lee, J. S. (2019). Prediction of car design perception using EEG and gaze patterns. IEEE Transactions on Affective Computing, 12(4), 843-856. [https://doi.org/10.1109/TAFFC.2019.2901733]
- 63 . Mostafavi, A. (2021). Architecture, biometrics, and virtual environments triangulation: a research review. Architectural Science Review, 1-18. [https://doi.org/10.1080/00038628.2021.2008300]
- 64 . Naghibi Rad, P., Shahroudi, A. A., Shabani, H., Ajami, S., & Lashgari, R. (2019). Encoding pleasant and unpleasant expression of the architectural window shapes: an ERP study. Frontiers in behavioral neuroscience, 186. [https://doi.org/10.3389/fnbeh.2019.00186]
- 65 . Nanda, U., Pati, D., Ghamari, H., & Bajema, R. (2013). Lessons from neuroscience: form follows function, emotions follow form. Intelligent Buildings International, 5(sup1), 61-78. [https://doi.org/10.1080/17508975.2013.807767]
- 66 . Nguyen, T. A., & Zeng, Y. (2014). A physiological study of relationship between designer’s mental effort and mental stress during conceptual design. Computer-Aided Design, 54, 3-18. [https://doi.org/10.1016/j.cad.2013.10.002]
- 67 . Nguyen, P., Nguyen, T. A., & Zeng, Y. (2018). Empirical approaches to quantifying effort, fatigue and concentration in the conceptual design process. Research in Engineering Design, 29(3), 393-409. [https://doi.org/10.1007/s00163-017-0273-4]
- 68 . Nguyen, P., Nguyen, T. A., & Zeng, Y. (2019). Segmentation of design protocol using EEG. Ai Edam, 33(1), 11-23. [https://doi.org/10.1017/S0890060417000622]
- 69 . Ohme, R., Reykowska, D., Wiener, D., & Choromanska, A. (2009). Analysis of neurophysiological reactions to advertising stimuli by means of EEG and galvanic skin response measures. Journal of Neuroscience, Psychology, and Economics, 2(1), 21. [https://doi.org/10.1037/a0015462]
- 70 . Pihko, E., Virtanen, A., Saarinen, V. M., Pannasch, S., Hirvenkari, L., Tossavainen, T., ... & Hari, R. (2011). Experiencing art: the influence of expertise and painting abstraction level. Frontiers in human neuroscience, 5, 94. [https://doi.org/10.3389/fnhum.2011.00094]
- 71 . Plassmann, H., Ramsøy, T. Z., & Milosavljevic, M. (2012). Branding the brain: A critical review and outlook. Journal of consumer psychology, 22(1), 18-36. [https://doi.org/10.1016/j.jcps.2011.11.010]
- 72 . Radder, H. (2009). The philosophy of scientific experimentation: a review. Automated Experimentation, 1(1), 1-8. [https://doi.org/10.1186/1759-4499-1-2]
- 73 . Shin, Y. B., Woo, S. H., Kim, D. H., Kim, J., Kim, J. J., & Park, J. Y. (2015). The effect on emotions and brain activity by the direct/indirect lighting in the residential environment. Neuroscience letters, 584, 28-32. [https://doi.org/10.1016/j.neulet.2014.09.046]
- 74 . Singh, Y., & Sharma, R. (2015). Individual alpha frequency (IAF) based quantitative EEG correlates of psychological stress. Indian J Physiol Pharmacol, 59(4), 414-421.
- 75 . Stolz, C., Endres, D., & Mueller, E. M. (2019). Threat-conditioned contexts modulate the late positive potential to faces-A mobile EEG/virtual reality study. Psychophysiology, 56(4), e13308. [https://doi.org/10.1111/psyp.13308]
- 76 . Solnais, C., Andreu-Perez, J., Sánchez-Fernández, J., & Andréu-Abela, J. (2013). The contribution of neuroscience to consumer research: A conceptual framework and empirical review. Journal of economic psychology, 36, 68-81. [https://doi.org/10.1016/j.joep.2013.02.011]
- 77 . Tóth, V. (2015). Measurement of stress intensity using EEG. Computer Science Engineering B. Sc. thesis, Budapest University of Technology and Economics, Faculty of Electrical Engineering and Informatics.
- 78 . Vartanian, O., & Goel, V. (2004). Neuroanatomical correlates of aesthetic preference for paintings. Neuroreport, 15(5), 893-897. [https://doi.org/10.1097/00001756-200404090-00032]
- 79 . Vecchiato, G., Tieri, G., Jelic, A., De Matteis, F., Maglione, A. G., & Babiloni, F. (2015). Electroencephalographic correlates of sensorimotor integration and embodiment during the appreciation of virtual architectural environments. Frontiers in psychology, 6, 1944. [https://doi.org/10.3389/fpsyg.2015.01944]
- 80 . Vieira, S., Benedek, M., Gero, J., Li, S., & Cascini, G. (2022). Brain activity in constrained and open design: the effect of gender on frequency bands. AI EDAM, 36. [https://doi.org/10.1017/S0890060421000202]
- 81 . Vieira, S., Gero, J. S., Delmoral, J., Gattol, V., Fernandes, C., Parente, M., & Fernandes, A. A. (2020). The neurophysiological activations of mechanical engineers and industrial designers while designing and problem-solving. Design Science, 6. [https://doi.org/10.1017/dsj.2020.26]
- 82 . Wang, Y. J., & Minor, M. S. (2008). Validity, reliability, and applicability of psychophysiological techniques in marketing research. Psychology & Marketing, 25(2), 197-232. [https://doi.org/10.1002/mar.20206]
- 83 . Xu, H., & Plataniotis, K. N. (2015, December). Subject independent affective states classification using EEG signals. In 2015 IEEE Global Conference on Signal and Information Processing (GlobalSIP) (pp. 1312-1316). IEEE. [https://doi.org/10.1109/GlobalSIP.2015.7418411]
- 84 . Yılmaz, B., Korkmaz, S., Arslan, D. B., Güngör, E., & Asyalı, M. H. (2014). Like/dislike analysis using EEG: determination of most discriminative channels and frequencies. Computer methods and programs in biomedicine, 113(2), 705-713. [https://doi.org/10.1016/j.cmpb.2013.11.010]
- 85 . Zhao, M., Jia, W., Yang, D., Nguyen, P., Nguyen, T. A., & Zeng, Y. (2020). A tEEG framework for studying designer’s cognitive and affective states. Design Science, 6. [https://doi.org/10.1017/dsj.2020.28]
- 86 . Zou, Z., Yu, X., & Ergan, S. (2019). Integrating biometric sensors, VR, and machine learning to classify EEG signals in alternative architecture designs. In Computing in civil engineering 2019: Visualization, information modeling, and simulation (pp. 169-176). Reston, VA: American Society of Civil Engineers. [https://doi.org/10.1061/9780784482421.022]
- 87 . Zhang, Z., Zhuo, K., Wei, W., Li, F., Yin, J., & Xu, L. (2021). Emotional Responses to the Visual Patterns of Urban Streets: Evidence from Physiological and Subjective Indicators. International Journal of Environmental Research and Public Health, 18(18), 9677. [https://doi.org/10.3390/ijerph18189677]