Like/dislike analysis using EEG: determination of most discriminative channels and frequencies

Using subjective emotion, facial expression, and gaze direction to evaluate user affective experience and predict preference when playing single-player games

The affective experience generated when users play computer games can influence their attitude and preference towards the game. Existing evaluation means mainly depend on subjective scales and physiological signals. However, some limitations should not be ignored (e.g. subjective scales are not objective, and physiological signals are complicated). In this paper, we 1) propose a novel method to assess user affective experience when playing single-player games based on pleasure-arousal-dominance (PAD) emotions, facial expressions, and gaze directions, and 2) build an artificial intelligence model to identify user preference. Fifty-four subjects participated in a basketball experiment with three difficulty levels. Their expressions, gaze directions, and subjective PAD emotions were collected and analysed. Experimental results showed that the expression intensities of angry, sad, and neutral, yaw angle degrees of gaze direction, and PAD emotions varied significantly under different difficulties. Besides, the proposed model achieved better performance than other machine-learning algorithms on the collected dataset.

Who takes the lead in consumer choices within romantic relationships: the evidence from electroencephalography hyperscanning and granger causality analysis

In real-life scenarios, joint consumption is common, particularly influenced by social relationships such as romantic ones. However, how romantic relationships affect consumption decisions and determine dominance remains unclear. This study employs electroencephalography hyperscanning to examine the neural dynamics of couples during joint-consumption decisions. Results show that couples, compared to friends and strangers, prefer healthier foods, while friends have significantly faster reaction times when selecting food. Time-frequency analysis indicates that couples exhibit significantly higher theta power, reflecting deeper emotional and cognitive involvement. Strangers show greater beta1 power, indicating increased cognitive effort and alertness due to unfamiliarity. Friends demonstrate higher alpha2 power when choosing unhealthy foods, suggesting increased cognitive inhibition. Inter-brain phase synchrony analysis reveals that couples display significantly higher inter-brain phase synchrony in the beta1 and theta bands across the frontal-central, parietal, and occipital regions, indicating more coordinated cognitive processing and stronger emotional bonds. Females in couples may be more influenced by emotions during consumption decisions, with detailed sensory information processing, while males exhibit higher cognitive control and spatial integration. Granger-causality analysis shows a pattern of male dominance and female dependence in joint consumption within romantic relationships. This study highlights gender-related neural synchronous patterns during joint consumption among couples, providing insights for further research in consumer decision-making.

A review on the use of eeg for the investigation of the factors that affect Consumer's behavior

This literature review surveys research papers that focused on the use of Electroencephalography (EEG) to study the impact of different factors in consumer behavior. The primary aim of this review is to determine which factors that affect consumer's behavior have already been evaluated in the existing literature and which remain unexplored. 118 papers are included in this survey. In order that the papers were analyzed in this review, a well-established neuromarketing experiment should have been performed indicating the methods of signals' acquisition, processing and analysis. The novelty of this work is that it considers and classifies not only research articles that studied a factor that influences consumers' choices, but also those that studied consumers' decisions as a result of the interactions that take place among the received marketing messages and the individual's internal or external environment. Findings indicated that the current approaches have mostly evaluated the effects of the promotional campaigns and product features to consumer's behavior. Also, it was shown that the effect of the interactions among different aspects that influence consumer behavior has not yet adequately been studied.

Primacy Effect of Dynamic Multi-Sensory Covid ADV Influences Cognitive and Emotional EEG Responses

Advertising uses sounds and dynamic images to provide visual, auditory, and tactile experiences, and to make the audience feel like the protagonist. During COVID-19, companies modified their communication by including pandemic references, but without penalizing multisensorial advertising. This study investigated how dynamic and emotional COVID-19-related advertising affects consumer cognitive and emotional responses. Nineteen participants, divided into two groups, watched three COVID-19-related and three non-COVID-19-related advertisements in two different orders (Order 1: COVID-19 and non-COVID-19; Order 2: non-COVID-19 and COVID-19), while electrophysiological data were collected. EEG showed theta activation in frontal and temporo-central areas when comparing Order 2 to Order 1, interpreted as cognitive control over salient emotional stimuli. An increase in alpha activity in parieto-occipital area was found in Order 2 compared to Order 1, suggesting an index of cognitive engagement. Higher beta activity in frontal area was observed for COVID-19 stimuli in Order 1 compared to Order 2, which can be defined as an indicator of high cognitive impact. Order 1 showed a greater beta activation in parieto-occipital area for non-COVID-19 stimuli compared to Order 2, as an index of reaction for painful images. This work suggests that order of exposure, more than advertising content, affects electrophysiological consumer responses, leading to a primacy effect.

An Ensemble Model for Consumer Emotion Prediction Using EEG Signals for Neuromarketing Applications

Traditional advertising techniques seek to govern the consumer's opinion toward a product, which may not reflect their actual behavior at the time of purchase. It is probable that advertisers misjudge consumer behavior because predicted opinions do not always correspond to consumers' actual purchase behaviors. Neuromarketing is the new paradigm of understanding customer buyer behavior and decision making, as well as the prediction of their gestures for product utilization through an unconscious process. Existing methods do not focus on effective preprocessing and classification techniques of electroencephalogram (EEG) signals, so in this study, an effective method for preprocessing and classification of EEG signals is proposed. The proposed method involves effective preprocessing of EEG signals by removing noise and a synthetic minority oversampling technique (SMOTE) to deal with the class imbalance problem. The dataset employed in this study is a publicly available neuromarketing dataset. Automated features were extracted by using a long short-term memory network (LSTM) and then concatenated with handcrafted features like power spectral density (PSD) and discrete wavelet transform (DWT) to create a complete feature set. The classification was done by using the proposed hybrid classifier that optimizes the weights of two machine learning classifiers and one deep learning classifier and classifies the data between like and dislike. The machine learning classifiers include the support vector machine (SVM), random forest (RF), and deep learning classifier (DNN). The proposed hybrid model outperforms other classifiers like RF, SVM, and DNN and achieves an accuracy of 96.89%. In the proposed method, accuracy, sensitivity, specificity, precision, and F1 score were computed to evaluate and compare the proposed method with recent state-of-the-art methods.

A systematic review of the prediction of consumer preference using EEG measures and machine-learning in neuromarketing research

Introduction

The present paper discusses the findings of a systematic review of EEG measures in neuromarketing, identifying which EEG measures are the most robust predictor of customer preference in neuromarketing. The review investigated which TF effect (e.g., theta-band power), and ERP component (e.g., N400) was most consistently reflective of self-reported preference. Machine-learning prediction also investigated, along with the use of EEG when combined with physiological measures such as eye-tracking.

Methods

Search terms ‘neuromarketing’ and ‘consumer neuroscience’ identified papers that used EEG measures. Publications were excluded if they were primarily written in a language other than English or were not published as journal articles (e.g., book chapters). 174 papers were included in the present review.

Results

Frontal alpha asymmetry (FAA) was the most reliable TF signal of preference and was able to differentiate positive from negative consumer responses. Similarly, the late positive potential (LPP) was the most reliable ERP component, reflecting conscious emotional evaluation of products and advertising. However, there was limited consistency across papers, with each measure showing mixed results when related to preference and purchase behaviour.

Conclusions and implications

FAA and the LPP were the most consistent markers of emotional responses to marketing stimuli, consumer preference and purchase intention. Predictive accuracy of FAA and the LPP was greatly improved through the use of machine-learning prediction, especially when combined with eye-tracking or facial expression analyses.

An intelligent neuromarketing system for predicting consumers' future choice from electroencephalography signals

Neuromarketing utilizes Brain-Computer Interface (BCI) technologies to provide insight into consumers responses on marketing stimuli. In order to achieve insight information, marketers spend about $400 billion annually on marketing, promotion, and advertisement using traditional marketing research tools. In addition, these tools like personal depth interviews, surveys, focus group discussions, etc. are expensive and frequently criticized for failing to extract actual consumer preferences. Neuromarketing, on the other hand, promises to overcome such constraints. In this work, an EEG-based neuromarketing framework is employed for predicting consumer future choice (affective attitude) while they view E-commerce products. After preprocessing, three types of features, namely, time, frequency, and time-frequency domain features are extracted. Then, wrapper-based Support Vector Machine-Recursive Feature Elimination (SVM-RFE) along with correlation bias reduction is used for feature selection. Lastly, we use SVM for categorizing positive affective attitude and negative affective attitude. Experiments show that the frontal cortex achieves the best accuracy of 98.67±2.98, 98±3.22, and 98.67±3.52 for 5-fold, 10-fold, and leave-one-subject-out (LOSO) respectively. In addition, among all the channels, F_z achieves best accuracy 90±7.81, 90.67±9.53, and 92.67±7.03 for 5-fold, 10-fold, and LOSO respectively. Subsequently, this work opens the door for implementing such a neuromarketing framework using consumer-grade devices in a real-life setting for marketers. As a result, it is evident that EEG-based neuromarketing technologies can assist brands and enterprises in forecasting future consumer preferences accurately. Hence, it will pave the way for the creation of an intelligent marketing assistive system for neuromarketing applications in future.

BCI-Based Consumers' Choice Prediction From EEG Signals: An Intelligent Neuromarketing Framework

Neuromarketing relies on Brain Computer Interface (BCI) technology to gain insight into how customers react to marketing stimuli. Marketers spend about $750 billion annually on traditional marketing camping. They use traditional marketing research procedures such as Personal Depth Interviews, Surveys, Focused Group Discussions, and so on, which are frequently criticized for failing to extract true consumer preferences. On the other hand, Neuromarketing promises to overcome such constraints. This work proposes a machine learning framework for predicting consumers' purchase intention (PI) and affective attitude (AA) from analyzing EEG signals. In this work, EEG signals are collected from 20 healthy participants while administering three advertising stimuli settings: product, endorsement, and promotion. After preprocessing, features are extracted in three domains (time, frequency, and time-frequency). Then, after selecting features using wrapper-based methods Recursive Feature Elimination, Support Vector Machine is used for categorizing positive and negative (AA and PI). The experimental results show that proposed framework achieves an accuracy of 84 and 87.00% for PI and AA ensuring the simulation of real-life results. In addition, AA and PI signals show N200 and N400 components when people tend to take decision after visualizing static advertisement. Moreover, negative AA signals shows more dispersion than positive AA signals. Furthermore, this work paves the way for implementing such a neuromarketing framework using consumer-grade EEG devices in a real-life setting. Therefore, it is evident that BCI-based neuromarketing technology can help brands and businesses effectively predict future consumer preferences. Hence, EEG-based neuromarketing technologies can assist brands and enterprizes in accurately forecasting future consumer preferences.

An EEG-Based Neuromarketing Approach for Analyzing the Preference of an Electric Car

This study evaluates consumer preference from the perspective of neuroscience when a choice is made among a number of cars, one of which is an electric car. Consumer neuroscience contributes to a systematic understanding of the underlying information processing and cognitions involved in choosing or preferring a product. This study aims to evaluate whether neural measures, which were implicitly extracted from brain activities, can be reliable or consistent with self-reported measures such as preference or liking. In an EEG-based experiment, the participants viewed images of automobiles and their specifications. Emotional and attentional stimuli and the participants' responses, in the form of decisions made, were meticulously distinguished and analyzed via signal processing techniques, statistical tests, and brain mapping tools. Long-range temporal correlations (LRTCs) were also calculated to investigate whether the preference of a product could affect the dynamic of neuronal fluctuations. Statistically significant spatiotemporal dynamical differences were then evaluated between those who select an electric car (which seemingly demands specific memory and long-term attention) and participants who choose other cars. The results showed increased PSD and central-parietal and central-frontal coherences at the alpha frequency band for those who selected the electric car. In addition, the findings showed the emergence of LRTCs or the ability of this group to integrate information over extended periods. Furthermore, the result of clustering subjects into two groups, using statistically significant discriminative EEG measures, was associated with the self-report data. The obtained results highlighted the promising role of intrinsically extracted measures on consumers' buying behavior.

Like/Dislike Prediction for Sport Shoes With Electroencephalography: An Application of Neuromarketing

Neuromarketing is an emerging research field for prospective businesses on consumer’s preference. Consumer’s preference prediction based on electroencephalography (EEG) can reliably predict likes or dislikes of a product. However, the current EEG prediction and classification accuracy have yet to reach ideal level. In addition, it is still unclear how different brain region information and different features such as power spectral density, brain asymmetry, differential entropy, and Hjorth parameters affect the prediction accuracy. Our study shows that by taking footwear products as an example, the recognition accuracy of product likes or dislikes reaches 94.22%. Compared with other brain regions, the features of the frontal and occipital brain region obtained a higher prediction accuracy, but the fusion of the features of the whole brain region could improve the prediction accuracy of likes or dislikes even further. Future work would be done to correlate the EEG-based like or dislike prediction results with product sales and self-reports.

A review of research on neuromarketing using content analysis: key approaches and new avenues

There is currently a growing interest in a deeper understanding of consumer behaviour. In this context, the union of different disciplines such as neuroscience and marketing has given birth to new fields of knowledge, e.g. neuromarketing. This study is mainly aimed at carrying out a systematic revision of the literature on neuromarketing from a holistic point of view, analysing its definition and processes, as well as more specific aspects such as its ethics and applications. Based on the results of our review, following a combined methodology with a base dictionary and text mining, our study presents both the current lines of research and the future lines of work.

Products With High Purchase Frequency Require Greater Inhibitory Control: An Event-Related Potential Study

One’s past behavior influences their present behavior. The effects of such response history have often been tested using response inhibition tasks. Since previous studies have highlighted the effect of immediate action history formed directly before the subsequent response in a laboratory environment, we aimed to elucidate the longer-term effects of response history, using repetitive and habitual consumer behavior in daily life as the response history. We used event-related potentials recorded in a Go/No-go task to investigate brain activity related to inhibitory control, hypothesizing that stimuli with a high frequency of choice in everyday life would elicit stronger inhibition-related activity, that is, the No-go-N2 component. Participants were asked to evaluate the frequency of purchase and use of some products, such as food and drink or social networking services (SNS) in everyday situations. Images of each product were assigned as stimuli in the Go and No-go trials according to the frequency of choice. The results showed that frequently purchased No-go stimuli yielded a larger amplitude of the No-go-N2 component and a negative shift between 200 and 300ms after the presentation of No-go stimuli. The results suggest that frequently chosen products evoke stronger inhibition conflicts and require greater cognitive control to withhold a response. Our findings showed that repeated purchase behavior in daily life forms a response history and has a long-term influence on the inhibition of even simple approaching behaviors, such as button pressing.

Picking Your Brains: Where and How Neuroscience Tools Can Enhance Marketing Research

The use of neuroscience tools to study consumer behavior and the decision making process in marketing has improved our understanding of cognitive, neuronal, and emotional mechanisms related to marketing-relevant behavior. However, knowledge about neuroscience tools that are used in consumer neuroscience research is scattered. In this article, we present the results of a literature review that aims to provide an overview of the available consumer neuroscience tools and classifies them according to their characteristics. We analyse a total of 219 full-texts in the area of consumer neuroscience. Our findings suggest that there are seven tools that are currently used in consumer neuroscience research. In particular, electroencephalography (EEG) and eye tracking (ET) are the most commonly used tools in the field. We also find that consumer neuroscience tools are used to study consumer preferences and behaviors in different marketing domains such as advertising, branding, online experience, pricing, product development and product experience. Finally, we identify two ready-to-use platforms, namely iMotions and GRAIL that can help in integrating the measurements of different consumer neuroscience tools simultaneously. Measuring brain activity and physiological responses on a common platform could help by (1) reducing time and costs for experiments and (2) linking cognitive and emotional aspects with neuronal processes. Overall, this article provides relevant input in setting directions for future research and for business applications in consumer neuroscience. We hope that this study will provide help to researchers and practitioners in identifying available, non-invasive and useful tools to study consumer behavior.

A comprehensive assessment of Brain Computer Interfaces: Recent trends and challenges

BACKGROUND

An uninterrupted channel of communication and control between the human brain and electronic processing units has led to an increased use of Brain Computer Interfaces (BCIs). This article attempts to present an all-encompassing review on BCI and the scientific advancements associated with it. The ultimate goal of this review is to provide a general overview of the BCI technology and to shed light on different aspects of BCIs. This review also underscores the applications, practical challenges and opportunities associated with BCI technology, which can be used to accelerate future developments in this field.

METHODS

This review is based on a systematic literature search for tracking down the relevant research annals and proceedings. Using a methodical search strategy, the search was carried out across major technical databases. The retrieved records were screened for their relevance and a total of 369 research chronicles were engulfed in this review based on the inclusion criteria.

RESULTS

This review describes the present scenario and recent advancements in BCI technology. It also identifies several application areas of BCI technology. This comprehensive review provides evidence that, while we are getting ever closer, significant challenges still exist for the development of BCIs that can seamlessly integrate with the user's biological system.

CONCLUSION

The findings of this review confirm the importance of BCI technology in various applications. It is concluded that BCI technology, still in its sprouting phase, requires significant explorations for further development.

Observing Pictures and Videos of Creative Products: An Eye Tracking Study

The paper offers insights into people’s exploration of creative products shown on a computer screen within the overall task of capturing artifacts’ original features and functions. In particular, the study presented here analyzes the effects of different forms of representations, i.e., static pictures and videos. While the relevance of changing stimuli’s forms of representation is acknowledged in both engineering design and human-computer interaction, scarce attention has been paid to this issue hitherto when creative products are in play. Six creative products have been presented to twenty-eight subjects through either pictures or videos in an Eye-Tracking-supported experiment. The results show that major attention is paid by people to original product features and functional elements when products are displayed by means of videos. This aspect is of paramount importance, as original shapes, parts, or characteristics of creative products might be inconsistent with people’s habits and cast doubts about their rationale and utility. In this sense, videos seemingly emphasize said original elements and likely lead to their explanation/resolution. Overall, the outcomes of the study strengthen the need to match appropriate forms of representation with different design stages in light of the needs for designs’ evaluation and testing user experience.

Neural Correlates of Preference: A Transmodal Validation Study

Liking is one of the most important psychological processes associated with the reward system, being involved in affective processing and pleasure/displeasure encoding. Currently, there is no consensus regarding the combination of physiological indicators which best predict liking, especially when applied to dynamic stimuli such as videos. There is a lack of a standard methodology to assess likeability over time and therefore in assessing narrative and semantic aspects of the stimulus. We developed a time-dependent method to evaluate the physiological correlates of likeability for three different thematic categories, namely: adventure (AV), comedy (CM), and nature landscape (LS). Twenty-eight healthy adults with ages ranging from 18 to 35 years (average: 23.85 years) were enrolled in the study. The participants were asked to provide likeability ratings for videos as they watched them, using a response box. Three 60-s videos were presented, one for each category, in randomized order while the participant’s physiological data [electroencephalogram (EEG), electrocardiogram (ECG) and eye tracking (ET)] was recorded. The comedy video (CM) presented the smallest minimum accumulated normalized rating (ANR; p = 0.013) and the LS video presented the highest maximum ANR (p = 0.039). The LS video presented the longest time for first response (p < 0.001) and the AV video presented the shortest time for maximum response (p = 0.016). The LS video had the highest mean likeability rating with 1.43 ± 2.31 points; and the CM video had the lowest with 0.57 ± 1.77. Multiple linear regression models were created to predict the likeability of each video using the following physiological indicators; AV: power in beta band at C4 and P4 (p = 0.004, adj. R² = 0.301); CM: alpha power in Fp2 (p = 0.001, adj. R² = 0.326) and LS: alpha power in P4, F8, and Fp2; beta power in C4 and P4 and pupil size, (p = 0.002, adj. R² = 0.489). Despite its limitations (e.g., using one 1-min video per category) our findings suggest that there is a considerable difference in the psychophysiological correlates of stimuli with different contextual properties and that the use of time-dependent methods to assess videos should be considered as best practices.

Neuronal Correlates of Product Feature Attractiveness

Decision-making is the process of selecting a logical choice from among the available options and happens as a complex process in the human brain. It is based on information processing and cost-analysis; it involves psychological factors, specifically, emotions. In addition to cost factors personal preferences have significant influence on decision making. For marketing purposes, it is interesting to know how these emotions are related to product acquisition decision and how to improve these products according to the user's preferences. For our proof-of-concept study, we use magneto- and electro-encephalography (MEG, EEG) to evaluate the very early reactions in the brain related to the emotions. Recordings from these methods are comprehensive sources of information to investigate neural processes of the human brain with good spatial- and excellent temporal resolution. Those characteristics make these methods suitable to examine the neurologic process that gives origin to human behavior and specifically, decision making. Literature describes some neuronal correlates for individual preferences, like asymmetrical distribution of frequency specific activity in frontal and prefrontal areas, which are associated with emotional processing. Such correlates could be used to objectively evaluate the pleasantness of product appearance and branding (i.e., logo), thus avoiding subjective bias. This study evaluates the effects of different product features on brain activity and whether these methods could potentially be used for marketing and product design. We analyzed the influence of color and fit of sports shirts, as well as a brand logo on the brain activity, specifically in frontal asymmetric activation. Measurements were performed using MEG and EEG with 10 healthy subjects. Images of t-shirts with different characteristics were presented on a screen. We recorded the subjective evaluation by asking for a positive, negative or neutral rating. The results showed significantly different responses between positively and negatively rated shirts. While the influence of the presence of a logo was present in behavioral data, but not in the neurocognitive data, the influence of shirt fit and color could be reconstructed in both data sets. This method may enable evaluation of subjective product preference.

Aesthetic preference recognition of 3D shapes using EEG

Recognition and identification of aesthetic preference is indispensable in industrial design. Humans tend to pursue products with aesthetic values and make buying decisions based on their aesthetic preferences. The existence of neuromarketing is to understand consumer responses toward marketing stimuli by using imaging techniques and recognition of physiological parameters. Numerous studies have been done to understand the relationship between human, art and aesthetics. In this paper, we present a novel preference-based measurement of user aesthetics using electroencephalogram (EEG) signals for virtual 3D shapes with motion. The 3D shapes are designed to appear like bracelets, which is generated by using the Gielis superformula. EEG signals were collected by using a medical grade device, the B-Alert X10 from advance brain monitoring, with a sampling frequency of 256 Hz and resolution of 16 bits. The signals obtained when viewing 3D bracelet shapes were decomposed into alpha, beta, theta, gamma and delta rhythm by using time-frequency analysis, then classified into two classes, namely like and dislike by using support vector machines and K-nearest neighbors (KNN) classifiers respectively. Classification accuracy of up to 80 % was obtained by using KNN with the alpha, theta and delta rhythms as the features extracted from frontal channels, Fz, F3 and F4 to classify two classes, like and dislike.