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The application of EEG power for the prediction and interpretation of consumer decision-making: A neuromarketing study
... Literature searches using the six journal databases used in the primary literature search were conducted using the search terms "consumer neuroscience", "neuromarketing", and "machine learning". From this literature search, an additional eighteen studies were identified which used machine learning to predict consumer preference or purchase intention using EEG signals [4,6,23,24,87,96,185,185,186,186,206,210,237,263,287,291,294,307,308,313]. These additional studies have been included in the systematic review section. ...
... Frontal asymmetry has also been found to be predictive self-reported preference [9, 110,118,119,122,148,191,192,260], and emerged as the only EEG TF measure that is consistently associated with behavioural measures of willingness to pay (WTP) [50,138,148,[217][218][219]239]. This suggests that frontal asymmetry may reflect actional/motivational responses to brands/products while evaluative ratings and recall may be better investigated using other TF measures such as relative-band power changes [26, 87,99,115,117,138,145,226,237]. For example, Ramsøy et al. [217] and Ramsøy et al. [218] showed using a principal component analysis that prefrontal asymmetry best accounted for variance in WTP, while other TF measures best accounted for self-reported preference. ...
... Early studies primarily used multivariate analysis methods, such as logistic regressions, to predict preference ratings [24, 85,101,139,237,303]. The subsequent use of machine-learning algorithms has been shown to improve the predictive accuracy of a model above the use of traditional logistic regressions [31, 85,87,96,102,103,151,185,185,186,186,251,255,287,291,294,313]. Most studies reviewed employed the use of multiple machine-learning algorithms, allowing for the direct comparison of these methods [2-4, 6, 23, 87, 98, 102, 103, 206, 210, 251, 254, 263, 287, 294, 307, 308] (see Table 4). ...
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.
... www.nature.com/scientificreports/ Many studies have utilized EEG concurrently with various decision-based tasks to investigate underlying neural mechanisms while making decisions 1,10,[15][16][17][18] . Different EEG characteristics were analyzed to assess the decision-induced effects. ...
... They observed substantial decreases in alpha power of parietal and occipital cortices in subjects making more creative moves. Golnar-Nik et al. 10 extracted different features from EEG power and demonstrated that the customer's decision to buy a product could be predicted using those features with high accuracy. Besides analyzing EEG oscillations of different frequency bands, many EEG studies on risk decision-making focused on analyzing event-related potentials (ERPs) [19][20][21] . ...
... Other techniques, including Independent Component Analysis (ICA) 25 , Linear Discriminant Analysis (LDA) 11 , or Canonical Correlation Analysis (CCA) 26 , were also employed to extract event-related salient features. These features can be used to predict people's future behavior or decisions based on the current outcomes 10,27 . ...
Decision-making is one of the most critical activities of human beings. To better understand the underlying neurocognitive mechanism while making decisions under an economic context, we designed a decision-making paradigm based on the newsvendor problem (NP) with two scenarios: low-profit margins as the more challenging scenario and high-profit margins as the less difficult one. The EEG signals were acquired from healthy humans while subjects were performing the task. We adopted the Correlated Component Analysis (CorrCA) method to identify linear combinations of EEG channels that maximize the correlation across subjects (n=23) or trials (n=40). The inter-subject or inter-trial correlation values (ISC or ITC) of the first three components were estimated to investigate the modulation of the task difficulty on subjects’ EEG signals and respective correlations. We also calculated the alpha- and beta-band power of the projection components obtained by the CorrCA to assess the brain responses across multiple task periods. Finally, the CorrCA forward models, which represent the scalp projections of the brain activities by the maximally correlated components, were further translated into source distributions of underlying cortical activity using the exact Low Resolution Electromagnetic Tomography Algorithm (eLORETA). Our results revealed strong and significant correlations in EEG signals among multiple subjects and trials during the more difficult decision-making task than the easier one. We also observed that the NP decision-making and feedback tasks desynchronized the normalized alpha and beta powers of the CorrCA components, reflecting the engagement state of subjects. Source localization results furthermore suggested several sources of neural activities during the NP decision-making process, including the dorsolateral prefrontal cortex, anterior PFC, orbitofrontal cortex, posterior cingulate cortex, and somatosensory association cortex.
... The different classifiers to understand consumer choices are reflected in [6], [7], [8], [9], [10]. A modelling framework to understand consumer choices in terms of 'likes' and 'dislikes' by analyzing EEG signals is proposed in [6], [7]. ...
... The conclusion obtained in this paper was that if the product is preferred, the value of Detrended Fluctuation Analysis (DFA) features of alpha waves will be high, and the DFA features of the beta waves will be low [9]. In [10] the potential of EEG spectral power for prediction of the customer's preferences is discussed along with the interpretation of alteration of consumer preferences in shopping behavior based on the content of the ad. It has been observed that the 'like' preference increased the EEG power of theta band in the left frontal region while 'dislike' preference increased the theta band power in the right frontal region. ...
... It helps ensure the inter-electrode spacing is equal. As in [10], the prefrontal cortex is used for decision making while the left dorsolateral prefrontal cortex is involved in perceptual decisions and the ventromedial prefrontal cortex is used while making value-based decisions. A single electrode was used to avoid excessive noise, and to reduce the dimensionality of the data recorded. ...
Neuromarketing is an emerging field that combines neuroscience and marketing to understand the factors that influence consumer decisions better. The study proposes a method to understand consumers' positive and negative reactions to advertisements (ads) and products by analysing electroencephalogram (EEG) signals. These signals are recorded using a low-cost single electrode headset from volunteers belonging to the ages 18-22. A detailed subject dependent (SD) and subject independent (SI) analysis was performed employing machine learning methods like Naive Bayes (NB), Support Vector Machine (SVM), k-nearest neighbour and Decision Tree and the proposed deep learning (DL) model. SVM and NB yielded an accuracy (Acc.) of 0.63 for the SD analysis. In SI analysis, SVM performed better for the advertisement, product and gender-based analysis. Furthermore, the performance of the DL model was on par with that of SVM, especially, in product and ads-based analysis.
... In the last 20 years, researchers proposed several automatic approaches with some of these considering the neurological mechanisms that drive marketing decision-making and contribute to the rapidly expanding field of neuromarketing research. In neuromarketing studies, researchers use biometric responses such as facial expression (Filipović et al., 2020), eye tracking (Khushaba et al., 2013), functional magnetic resonance imaging (fMRI) (Hsu and Cheng, 2018), galvanic skin response (Ohira and Hirao, 2015), and electroencephalography (EEG) (Golnar-Nik et al., 2019), magnetoencephalograpy (MEG) (Hege et al., 2014) to extract customers' insights. Previously, the neuromarketing community was primarily interested in fMRI, which assesses cerebral blood flow imaging and aids in the identification of areas triggered by stimuli (Rawnaque et al., 2020). ...
... Using these features, they calculated preference indices which was later used to train multiple machine learning and deep learning (DL) models for the classification of EEG signals. Golnar-Nik et al. (2019) employed LDA and SVM classifiers to assess how effectively EEG signals could distinguish various customer preferences and predict the occurrence of decision-making in another study. Telpaz et al. (2015) published one of the most influential research articles in the field of neuromarketing in 2015. ...
... So, this work represents those features that captures distinct firing pattern from EEG signals. Previously, the power and some statistical features were widely used in Neuromarketing works (Golnar-Nik et al., 2019;Yadava et al., 2017). Along with these, we increased the feature set to capture more subtle changes in the EEG for the mixed stimuli setting. ...
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.
... In the last 20 years, several types of automatic approaches had been proposed for use in marketing, with some of these approaches taking into account the neurological mechanisms that underpinned marketing decision-making and helped the growth of neuromarketing research. Facial expression [6], eye tracking [7], functional magnetic resonance imaging (fMRI) [8], galvanic skin reaction [9], and electroencephalography (EEG) [10] are some of the biometric responses that have been used to track customer insights in neuromarketing studies. ...
... Furthermore, the cumbersome process of collecting EEG signals also makes the process harder to implement such technology in the real world market. Furthermore, while analyzing the EEG signals, most of the previous works focused on a limited number of features like power [10] which correlates with the preference of the consumers, however, the approach lacks prediction of the preference with confidence. Therefore, in light of the research problem, the following research questions are to be addressed: ...
... In another instance, researchers employed SVM and KNN to measure user preferences for aesthetics shown as virtual three-dimensional objects, with frequency bands serving as characteristics for EEG categorization into binary classes [5]. Again in another research, Golnar-nik et al. [10] used Linear discriminant analysis (LDA) and SVM classifiers to evaluate how well the power of EEG signals could identify distinct customer preferences and predict the occurrence of decision-making. As the application of deep learning (DL) is growing in different fields [17,18], DL is also used to test its feasibility in EEG 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, Fz 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.
... Therefore, it is necessary to apply external signals to discriminate the selective decision-making intention of the user. In recent years, Brain-Computer Interface (BCI) technology has been developed rapidly, and a series of EEG components related to selective decision-making process have been found with experiments [7,8,9]. Thus there is some correlation between EEG signals and decision-making intention in object selection tasks. ...
... When the whitened EEG is multiplied by the eigenvectors corresponding to the eigenvalues λ 1 and λ 2 , the EEG data is filtered in the spatial domain, and original data is projected in a new space. The spatial filter matrix can be calculated by equation (3)(4)(5)(6)(7)(8)(9)(10). ...
... After being spatial projected by Equations (3)(4)(5)(6)(7)(8)(9)(10)(11) and (3)(4)(5)(6)(7)(8)(9)(10)(11)(12), the variance difference between the positive data and negative data is amplified. Therefore, the projected EEG data Z 1 and Z 2 can be applied to obtain the spatial features f 1 and f 2 of the two types of signals. ...
Currently, building natural interaction systems based on physiological signals has become an crucial requirement for the development of Computer Aided Design (CAD). As the first step of model operation in CAD, object selection is essential and the efficiency of selecting has a great impact on the experience of users. In the research community, gaze-based interaction for object selection has been well-established. However, this interactive mode is still imperfect due to Midas touch problem. In this work, a selection intention discrimination (SID) model is implemented to decode electroencephalogram (EEG) signals generated during object selection process. Common Spatial Pattern (CSP) is applied to extract spatial features from EEG in four frequency bands. Then these features are learned by a Convolutional Neural Network (CNN) equipped with an adaptive weights training module to realize the SID. To verify the decoding feasibility of this model, a cognitive experiment related to object selection is conducted. The empirical result shows that the performance of this model is good. It turns out that EEG-based object selection is feasible, which can be a intuitive and natural interaction mode for CAD.
... In neuromarketing, power spectral density (PSD) is one of the most common feature extraction method (Khushaba et al., 2012;Yılmaz et al., 2014). Some studies theorize that the PSD of EEG signals can be used to identify the likes and dislikes of products (Golnar-Nik et al., 2019). Additionally, some studies presume that features and parameters of frontal brain asymmetry, such as approach-withdrawal (AW) index, effort index, choice index, valence, can expertly identify product preference of consumers (Cartocci et al., 2017;Ramsøy et al., 2018;Aldayel et al., 2020Aldayel et al., , 2021. ...
... The EEG features of PSD, brain asymmetry, DE, and Hjorth parameter were chosen in this study. In neuromarketing, PSD is one of the most common feature extraction methods (Khushaba et al., 2012;Yılmaz et al., 2014;Golnar-Nik et al., 2019). The brain asymmetry-based preference indices such as approach-withdrawal (AW) index, valence, choice index, and effort index are also used as features to predict consumer's preference (Aldayel et al., 2020(Aldayel et al., , 2021. ...
... In addition, the significant power difference in the beta frequency band is found in the frontal, temporal, central, parietal, and occipital regions. These findings are similar to other studies (Golnar-Nik et al., 2019). Figure 7 shows the classification results of four different classifiers based on four different feature sets. ...
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.
... With the help of quantitative measurement in neuroscience such as electroencephalogram (EEG), these relatively vague concepts can be analyzed and furtherly explore consumer decisions. The most recent studies in neuromarketing have been focused on consumer's purchase intention and decisions (purchase vs. nopurchase) (Shang et al., 2020;Wang et al., 2021), or predicting consumer's purchases (Golnar-Nik et al., 2019;Bak et al., 2022). But fewer studies have gone beyond "buy" and "no buy" and the relative contribution of cognitive and emotional process on consumer motivation (Aditya and Sarno, 2018;Royo-Vela and Varga, 2022) and the distinction of consumers decisions have not been thoroughly investigated. ...
... This is in line with our assumption and prior findings and suggests that planned behavior is reflected as a theta-alpha activity ratio even in a virtual shopping environment. As opposed to most of the research in the field of consumer neuroscience, which mainly focus on consumers' "purchase" and "no purchase" (Rosenlacher et al., 2018;Golnar-Nik et al., 2019;Goto et al., 2019;Eichhorn et al., 2021;Melendrez-Ruiz et al., 2021;Horr et al., 2022) we have extended this view by looking deeper to the relative contribution of cognitive and emotional response and the dual-process of consumer's decision-making process. Additionally, one of the important contributions of this study is that, compared to highly controlled lab experiments, it adds a higher ecological validity to the research by using an immersive Frontiers in Neuroscience 07 frontiersin.org ...
Introduction
Consumer decision-making processes involve a complex interrelation between perception, emotion, and cognition. Despite a vast and diverse literature, little effort has been invested in investigating the neural mechanism behind such processes.
Methods
In the present work, our interest was to investigate whether asymmetrical activation of the frontal lobe of the brain could help to characterize consumer’s choices. To obtain stronger experimental control, we devised an experiment in a virtual reality retail store, while simultaneously recording participant brain responses using electroencephalogram (EEG). During the virtual store test, participants completed two tasks; first, to choose items from a predefined shopping list, a phase we termed as “planned purchase”. Second, subjects were instructed that they could also choose products that were not on the list, which we labeled as “unplanned purchase.” We assumed that the planned purchases were associated with a stronger cognitive engagement, and the second task was more reliant on immediate emotional responses.
Results
By analyzing the EEG data based on frontal asymmetry measures, we find that frontal asymmetry in the gamma band reflected the distinction between planned and unplanned decisions, where unplanned purchases were accompanied by stronger asymmetry deflections (relative frontal left activity was higher). In addition, frontal asymmetry in the alpha, beta, and gamma ranges illustrate clear differences between choices and no-choices periods during the shopping tasks.
Discussion
These results are discussed in light of the distinction between planned and unplanned purchase in consumer situations, how this is reflected in the relative cognitive and emotional brain responses, and more generally how this can influence research in the emerging area of virtual and augmented shopping.
... Out of which, visual and gustatory play a major role in processing neuromarketing techniques for beverage products. The brain waves are labeled based on the order of oscillating frequency as delta, alpha, theta, beta [3]. There is cortical activity in the frequency range of theta and an increase in theta band in subjects when they watch advertisements they like [4]. ...
... The scalp distance is measured and the electrodes are placed as per the measurements. The range of low pass, high pass, notch filter is 70Hz, 1 Hz, 50 Hz respectively and the regions focused are the frontal and parietal lobes [3]. The gain was set to 7.5 μV/mm and the sweep speed was set to 30 mm/S when recording EEG signals. ...
Neuromarketing merges viewpoints of marketing, neuroscience, economics, choice hypothesis that are required to analyze the psychology of consumers’ preference to product development. The traditional methods involve product ratings, conducting questionnaire surveys that stumble upon verbal declarations provided by the vendees. Consumer Neuroscience describes the emotional, cognitive aspects that form the base of human decision making. Our study aims to utilize the neuroscientific information that distinguishes contrasts between healthy subjects’ EEG signals for examining the brain activity during visual and gustatory stimuli of different flavours of a beverage brand. The EEG montage assigned according to brain-region-specific localization draws out the subjects’ true elicited subconscious response regardless of whether the subject attempts to control his/her affective state. The results showed the activation of theta and delta bands of EEG signals during the given stimuli. These elicited signal variations can be used to identify the best favoured item for successful product dispatch and reduction in loss. Another major application is directed towards the customization of liquid food intake of locked-in, comatose, vegetative state patients by observing their brain response to the various fluid intake and determining the best response among them. This aids physicians to put the patients on a path to recovery.
... Out of which, visual and gustatory play a major role in processing neuromarketing techniques for beverage products. The brain waves are labeled based on the order of oscillating frequency as delta, alpha, theta, beta [3]. There is cortical activity in the frequency range of theta and an increase in theta band in subjects when they watch advertisements they like [4]. ...
... The scalp distance is measured and the electrodes are placed as per the measurements. The range of low pass, high pass, notch filter is 70Hz, 1 Hz, 50 Hz respectively and the regions focused are the frontal and parietal lobes [3]. The gain was set to 7.5 μV/mm and the sweep speed was set to 30 mm/S when recording EEG signals. ...
Neuromarketing merges viewpoints of marketing, neuroscience, economics, choice
hypothesis that are required to analyze the psychology of consumers’ preference to product development. The traditional methods involve product ratings, conducting questionnaire surveys that stumble upon the walls of verbal declarations of the vendees. By assessing the psychological conditions of purchasers, Consumer Neuroscience explains the cognitive and emotional aspects that form the base of human decision making. To detect successive changes in brain activity continuously in time, a versatile approach such as Electroencephalogram (EEG) can thrive to be a compass for patron thoughts. Our study aims to utilize the neuroscientific information that
distinguishes contrasts between healthy subjects' EEG signals for examining the brain activity during visual and gustatory stimuli of different flavours of a beverage brand. The EEG montage assigned according to brain-region-specific localization draws out the subjects’ true elicited subconscious response regardless of whether the subject attempts to control his/her affective state. The results showed the activation of theta and delta bands of EEG signals during the given stimuli. These elicited signal variations can be used to identify the best favoured item for successful product dispatch and reduction in loss. Another major application of this study is directed towards the customization of liquid food intake of locked-in, comatose, vegetative state patients by observing their brain response to the various fluid intake and determining the best response among them. This aids physicians to put the patients on a path to recovery.
... Given that marketing has changed considerably, researchers are adapting to the multidimensional vision of consumers, addressing concepts such as emotions, prejudices and values in studies, so far several types of automated methods applied for research purposes have been developed (Stasi et al., 2017). Some of the most interesting ones address the neural mechanisms that underlie the decision-making process and have evolved in the field of marketing research, which is constantly evolving (Golnar-Nik et al., 2019). These include measurements to monitor biometric responses, including eye tracking, facial expression, galvanic skin response, functional magnetic resonance imaging (fMRI), magnetic resonance imaging (MRI), cranial magnetic stimulation (TMS), and finally, electroencelography. (EEG), which has gained special attention in recent years (Ait Hammou et al., 2013;Šola, 2021). ...
... The use of these technologies underlies neuromarketing (or 'consumer neuroscience' (Smidts et al., 2014) an emerging field that combines perspectives from marketing, neuroscience, economics, decision theory and psychology. Neuromarketing uses brain imaging technology to effectively reveal the underlying reasons for consumer behaviour and to predict decision-making processes (Thabani & Wellington, 2017), (Hubert & Kenning, 2008), (Senior & Lee, 2008), (Stasi et al., 2017), and (Ma~ Nas-Viniegra et al., 2020) support the potential of discovering automatic brain processes that determine purchasing decisions with major effects for companies and society, and the literature argues that information obtained through the analysis of biometric responses may be more reliable than that obtained through questionnaires (Golnar-Nik et al., 2019). While traditional marketing can yield results in understanding consumers' needs, neuromarketing covers their true desires and the emotional effect that products or services have on the brain. ...
Since 1970, the intensity of implementing budgetary policies in the two categories of states, developed states, and emerging states, has been observed in Europe. These policies had the effect of increasing the levels of taxation, also called progressive taxation, at that time. To stop this phenomenon of progressive taxation, the Maastricht Treaty and then the Stability and Growth Pact have had the effect of forcing states to adopt different fiscal policies to reduce the feeling of fiscal pressure made necessary by budgetary deficiencies and public debt accumulated over time. In fact, since the 1990s, each Member State of the European Union has interpreted the treaties mentioned above separately; some states reduced spending, while others preferred to impose more significant fiscal pressure on citizens by raising taxes and fees. In our article, we analyze and disseminate the general fiscal pressure of some developed countries in the European Union. We identify the economic priority indicators that influence the phenomenon of fiscal pressure, such as the level of direct taxes, indirect taxes, the growth rate of gross domestic product, and the level of public debt, providing an overview of economic development over the last three decades.To this end, the authors have developed an econometric model that captures the factors that influence the fiscal pressure in several developed countries of the European Union in the period 1995-2018.
... Given that marketing has changed considerably, researchers are adapting to the multidimensional vision of consumers, addressing concepts such as emotions, prejudices and values in studies, so far several types of automated methods applied for research purposes have been developed (Stasi et al., 2017). Some of the most interesting ones address the neural mechanisms that underlie the decision-making process and have evolved in the field of marketing research, which is constantly evolving (Golnar-Nik et al., 2019). These include measurements to monitor biometric responses, including eye tracking, facial expression, galvanic skin response, functional magnetic resonance imaging (fMRI), magnetic resonance imaging (MRI), cranial magnetic stimulation (TMS), and finally, electroencelography. (EEG), which has gained special attention in recent years (Ait Hammou et al., 2013;Šola, 2021). ...
... The use of these technologies underlies neuromarketing (or 'consumer neuroscience' (Smidts et al., 2014) an emerging field that combines perspectives from marketing, neuroscience, economics, decision theory and psychology. Neuromarketing uses brain imaging technology to effectively reveal the underlying reasons for consumer behaviour and to predict decision-making processes (Thabani & Wellington, 2017), (Hubert & Kenning, 2008), (Senior & Lee, 2008), (Stasi et al., 2017), and (Ma~ Nas-Viniegra et al., 2020) support the potential of discovering automatic brain processes that determine purchasing decisions with major effects for companies and society, and the literature argues that information obtained through the analysis of biometric responses may be more reliable than that obtained through questionnaires (Golnar-Nik et al., 2019). While traditional marketing can yield results in understanding consumers' needs, neuromarketing covers their true desires and the emotional effect that products or services have on the brain. ...
The scientists address the decision-making process by introducing the
need of incorporating the neuroscience as a tool for marketing research field. The
concern of the authors for this topic pursued to interpose the neuromarketing
advanced techniques in companies’ business growth strategies. This literature
summary presents a shifting traditional marketing that underlies the major
implications of advanced techniques to study the true desires and the emotional effect
that products or services have on the brain to obtain the competitive advantage.
Loyalty, trust, and the true reasons why consumers buy from a particular brand can
be reached if the companies understand the impact on their growth. Doubtless, the
challenges that companies can face affect profit growth and the long-term
development, but a prepared academic environment with modern education-based
models and ready for support can control any issue.
... Defense Advanced Research Projects Agency launched an ambitious 'Next-Generation Non-surgical Neurotechnology' (N3) program in 2019, aimed to develop high-performance noninvasive BCI technology comparable to the existing invasive one [1]. Various real-life BCIs are currently emerging, including neuromarketing [2,3], neurofeedback training [4,5], ubiquitous monitoring [6][7][8], and intelligent control [9,10]. In the future, non-invasive BCI can become a fascinating entrance to the next-generation metaverse after virtual reality (VR) and augmented reality (AR). ...
Objective:
Reliable and user-friendly electrodes can continuously and real-time capture the electroencephalography signals, which is essential for real-life brain-computer interfaces (BCIs). This study develops a flexible, durable, and low-contact-impedance polyvinyl alcohol/polyacrylamide double-network hydrogel (PVA/PAM DNH)-based semi-dry electrode for robust electroencephalography recording at hairy scalp.
Approach:
The PVA/PAM DNHs are developed using a cyclic freeze-thaw strategy and used as a saline reservoir for semi-dry electrodes. The PVA/PAM DNHs steadily deliver trace amounts of saline onto the scalp, enabling low and stable electrode-scalp impedance. The hydrogel also conforms well to the wet scalp, stabilizing the electrode-scalp interface. The feasibility of the real-life BCIs is validated by conducting four classic BCI paradigms on 16 participants.
Main results:
The results show that the PVA/PAM DNHs with 7.5%wt% PVA achieve a satisfactory trade-off between the saline load-unloading capacity and the compressive strength. The proposed semi-dry electrode exhibits a low contact impedance (18 ± 8.9 kΩ at 10 Hz), a small offset potential (0.46 mV), and negligible potential drift (1.5 ± 0.4 μV/min). The temporal cross-correlation between the semi-dry and wet electrodes is 0.91, and the spectral coherence is higher than 0.90 at frequencies below 45 Hz. Furthermore, no significant differences are present in BCI classification accuracy between these two typical electrodes.
Significance:
Based on the durability, rapid setup, wear-comfort, and robust signals of the developed hydrogel, PVA/PAM DNH-based semi-dry electrodes are a promising alternative to wet electrodes in real-life BCIs.
... It should be emphasized that the Attention here refers to the parameters rather than the attention mechanism in the model. The ratio of β wave energy to θ wave energy in EEG signals can show people's attention to things to a certain extent [42]. However, the classification based on energy ratio alone has poor precision, and EEG signals tend to be disordered in a nonconcentrated state, which will increase the complexity of features. ...
... Все больше исследований в области маркетинговых исследований применяют методы нейровизуализации для выявления нейронного происхождения процессов принятия решений потребителями в контексте исследования влияния следующих стимулов: упаковки продукта [24,26], цены [27], рекламы [28,29], брендинга и среды электронной коммерции [30,31]. ...
... Research in consumer neuroscience has demonstrated that EEG recordings of consumers' emotional states can provide highly correlated information regarding their product preferences and liking. Such knowledge describing consumer behavior is now being portrayed as a decisive aspect of contemporary marketing schemes [9]. In EEG classification tasks the major challenge lies within the dilemma of feature extraction. ...
Electroencephalogram (EEG)-based emotion recognition is a computationally challenging issue in the field of medical data science that has interesting applications in cognitive state disclosure. Generally, EEG signals are classified from frequency-based features that are often extracted using non-parametric models such as Welch’s power spectral density (PSD). These non-parametric methods are not computationally sound due to having complexity and extended run time. The main purpose of this work is to apply the multiple signal classification (MUSIC) model, a parametric-based frequency-spectrum-estimation technique to extract features from multichannel EEG signals for emotional state classification from the SEED dataset. The main challenge of using MUSIC in EEG feature extraction is to tune its parameters for getting the discriminative features from different classes, which is a significant contribution of this work. Another contribution is to show some flaws of this dataset for the first time that contributed to achieving high classification accuracy in previous research works. This work used MUSIC features to classify three emotional states and achieve 97% accuracy on average using an artificial neural network. The proposed MUSIC model optimizes a 95–96% run time compared with the conventional classical non-parametric technique (Welch’s PSD) for feature extraction.
... El branding y el comportamiento del consumidor son ejes transversales en los estudios de marketing (Aaker, 1991;Keller, 2002;Solomon et al., 2017;Sandoval et al., 2018), los cuales han permeado diferentes disciplinas y escenarios emergentes como lo son la gestión humana (López-Rodríguez & Neme-Chaves, 2021); las neurociencias aplicadas al estudio del consumo (Golnar-Nik et al., 2019;Ortegon-Cortazar, 2019), la internacionalización de servicios (López-Rodríguez et al., 2020), la psicología del consumidor (Saad, 2020) y el marketing político (Pal, 2017); de ahí la pertinencia de identificar la manera en la que estos ejes han sido estudiados en el segmento de adulto mayor. ...
El presente artículo tiene como objetivo identificar las tendencias de investigación en branding y construcción de las marcas enfocadas al adulto mayor, considerando la importancia del conocimiento de este segmento de consumo, a partir de los cambios sociodemográficos correspondientes a la estructura poblacional en el mundo. Para esto, se llevó a cabo un análisis bibliométrico a partir de la base de datos Scopus y desarrollado con el software R Core Team 2020 - Bibliometrix y posteriormente se realizó una revisión sistemática cualitativa utilizando el protocolo PRISMA. Los resultados muestran que existe un interés reciente por la investigación en torno al estudio del branding y de temas afines del análisis del consumidor del segmento de adulto mayor, aunque aún posee un bajo nivel de producción científica. Los hallazgos identifican como temas más relevantes aquellos asociados con el valor de marca, la imagen de marca, la orientación al mercado, las estrategias de marketing relacional, la segmentación del mercado y el comportamiento del consumidor mayor. Respecto a la imagen de marca, se aprecia que los aspectos más característicos de los estudios se relacionan con la psicografía de la edad, los procesos cognitivos, la autoconfianza y la audacia. A partir de la revisión previa se reconoce la pertinencia de realizar mayores esfuerzos investigativos para responder a la necesidad de conocimiento acerca del comportamiento del consumidor adulto mayor, dado que son escasos los trabajos que permiten identificar las influencias, procesos y patrones de comportamiento de este segmento de mercado en el mundo.
... Bu iki seçimde de kullanılacak yöntemler invazif müdahale gerektiren ve invazif olmayan teknikler olarak ayrılabilir. Ancak belirtmek gerekir ki son yıllarda yapılan nöropazarlama çalışmalarının büyük kısmı invazif olmayan tekniklerle yapılmaktadır (Golnar-Nik, Farashi & Safari, 2019;Aldayel vd., 2020;Hsu & Chen, 2020). ...
Hizmetin özellikleri (soyutluk, ayrılmazlık vb.) ve tüketicilerin duygusal, bilinçsiz ve bilinçaltı durumlarından dolayı, turizm pa-zarlaması karmaşık bir olgudur (Boz vd., 2017). Turizm endüstrisi içerisinde işletmelerin içinde bulunduğu yoğun rekabet ortamı da düşünüldüğünde, pazarlamaya yönelik karmaşık analizleri doğru yapabilmek işletmeler için hayati önem taşımaktadır. Pazarlama araştırmaları, yöneticilere ihtiyaç ve talepleri anlama konusunda önemli veriler sunmakla beraber, kullanılan geleneksel yöntemle-rin başarısı sorgulanabilmektedir. Katılımcıların farklı nedenlerle kendini doğru ifade etmemesi veya edememesi hatalı pazarlama faaliyetlerine neden olabilmekte, bu da kaynakların verimsiz kul-lanılmasının yanı sıra, mevcut müşterilerin dahi kaybedilmesine neden olabilmektedir. Bu durum, pazarlamacılarda "tüketici ger-çekte ne ister?" sorusunun doğmasına neden olmuştur. Pazarlamadaki en önemli sorulardan bir diğeri ise tüketicileri bir ürün yerine başka bir ürüne karar vermeye iten şeyin ne olduğu-dur (Jordão vd., 2017). Pepsi yerine neden Coca Cola tercih edil-mektedir? Kadınlar neden bilimkurgu filmlerini tercih etmez? Er-kekler neden spor arabaları tercih eder? İşletmeler tüketicileri sa-tın almaya ikna etmek için bu tür soruları yanıtlamaya çalışmalı ve her zaman tüketicilerin nasıl düşündüklerini öğrenmenin yeni yollarını bulmalıdır (Ciprian-Marcel vd., 2004).
... Electroencephalography (EEG)-based brain−computer interfaces (BCIs) have received ever-growing attention from global researchers and investors recently. Apart from reliable EEG signals, real-world EEG-based BCIs, such as physiological monitoring, 1-3 neurofeedback training, 4,5 intelligent control, 6,7 and neuromarketing, 8,9 attach great importance to user-friendliness. With the continuous breakthroughs in microelectronics and signal processing, the development of BCIs, especially noninvasive BCIs, has recently entered a fast lane. ...
Herein, we fabricated a flexible semi-dry electrode with excellent mechanical performance, satisfactory self-adhesive, and low contact impedance using a physical/chemical crosslinked polyvinyl alcohol/polyacrylamide dual-network hydrogel (PVA/PAM DNH) as an efficient saline reservoir. The resultant PVA/PAM DNHs showed admirable adhesive and compliant to the hairy scalp, facilitating the establishment a robust electrode/skin interface for signal transmission. Moreover, the PVA/PAM DNHs steadily released trace saline onto the scalp to achieve the minimized potential drift (1.47 ± 0.39 μV min-1) and low electrode-scalp impedance (18.2 ± 8.9 kΩ @ 10 Hz). More importantly, the application feasibility of real-world BCIs was adequately validated by ten participants using two classic BCI paradigms. The mean temporal cross-correlation coefficient between the semi-dry and wet electrodes in the eyes open/closed and the N200 speller paradigms are 0.919 ± 0.054 and 0.912 ± 0.050, respectively. Both two electrodes demonstrate anticipated neuroelectrophysiological responses with similar patterns. This semi-dry electrode could also effectively capture robust P-QRS-T peaks during electrocardiogram (ECG) recording. Considering their outstanding advantages of fast setup, user-friendliness and robust signals, the proposed PVA/PAM DNH-based electrode is a promising alternative to wet electrodes in real-life BCIs.
... Similarly, FAA is an important measurement in consumer neuroscience; studies have shown that FAA dynamics when subjects watch images of products are related to sales (Baldo et al., 2015), willingness to buy (Golnar-Nik et al., 2019), and individual preference (Touchette and Lee, 2017;Di Gruttola et al., 2021). FAA measured during sub-sections of commercials was also related to individual preference (Ohme et al., 2010;Vecchiato et al., 2014), investment decision-making (Di Gruttola et al., 2021), and consumers' product choice (Golnar-Nik et al., 2019). Moreover, the reverse effect has also been verified in neurofeedback studies, where users learned to upregulate their frontal alpha asymmetry, which reduced anxiety (Mennella et al., 2017) or increased ratings of neutral and positive films as more positive (Allen et al., 2001). ...
Most consumers are aware that climate change is a growing problem and admit that action is needed. However, research shows that consumers’ behavior often does not conform to their value and orientations. This value-behavior gap is due to contextual factors such as price, product design, and social norms as well as individual factors such as personal and hedonic values, environmental beliefs, and the workload capacity an individual can handle. Because of this conflict of interest, consumers have a hard time identifying the true drivers of their behavior, as they are either unaware of or unwilling to acknowledge the processes at play. Therefore, consumer neuroscience methods might provide a valuable tool to uncover the implicit measurements of pro-environmental behavior (PEB). Several studies have already defined neurophysiological differences between green and non-green individuals; however, a behavior change intervention must be developed to motivate PEB among consumers. Motivating behavior with reward or punishment will most likely get users engaged in climate change action via brain structures related to the reward system, such as the amygdala, nucleus accumbens, and (pre)frontal cortex, where the reward information and subsequent affective responses are encoded. The intensity of the reward experience can be increased when the consumer is consciously considering the action to achieve it. This makes goal-directed behavior the potential aim of behavior change interventions. This article provides an extensive review of the neuroscientific evidence for consumer attitude, behavior, and decision-making processes in the light of sustainability incentives for behavior change interventions. Based on this review, we aim to unite the current theories and provide future research directions to exploit the power of affective conditioning and neuroscience methods for promoting PEB engagement.
... Nik PG based on the key link, from the perspective of consumer behavior process problems [20]. This paper discusses the marketing strategy and marketing strategy combination that enterprises should adopt to carry out online marketing, aiming to provide guidance for enterprises to carry out online marketing. ...
This paper divides the research modes of consumer purchase behavior characteristics into three categories: experience-driven mode, theory-driven mode, and data-driven mode. An analysis algorithm based on customer consumption behavior is proposed, and the idea of combining customer consumption behavior factors such as satisfaction and loyalty is proposed. Through comparison, it is pointed out that the data-driven model is most suitable for analyzing the characteristics of online consumers’ purchasing behavior. Using the decision support of knowledge base, different service schemes for customers with different evaluation degrees are realized. In order to improve the accuracy of sample classification and maximize the output function, genetic algorithm is used to optimize the samples. A deep neural network structure algorithm is proposed to classify customer transaction data samples. In this algorithm, the sheep nodes are not fixed, but the number of hidden layers and unit nodes of the neural network are dynamically determined according to the sample training. The research excavates various kinds of valuable information such as consumer preferences and consumption structure from the huge consumption data of consumers. It is not only helpful for enterprises to analyze consumers’ consumption behavior and organize production but also helpful for enterprises to realize the concept of personalization.
... These regions, within social contexts, have been linked to susceptibility to social influence from peers (for a comprehensive review, see Falk & Scholz, 2018). More broadly in EEG, several frequency bands have been implicated in decision-making (e.g., Nakao et al., 2019), but often show some specificity in frontal and parietal regions (e.g., Golnar-Nik et al., 2019). Interestingly, a recent study inspecting long-range temporal correlations in EEG recordings has shown a relationship between theta to alpha bands and the abstract concept of self-identity and identity confusion (Sugimura et al., 2021). ...
Since their development, social media has grown as a source of information and has a significant impact on opinion formation. Individuals interact with others and content via social media platforms in a variety of ways, but it remains unclear how decision-making and associated neural processes are impacted by the online sharing of informational content, from factual to fabricated. Here, we use EEG to estimate dynamic reconfigurations of brain networks and probe the neural changes underlying opinion change (or formation) within individuals interacting with a simulated social media platform. Our findings indicate that the individuals who changed their opinions are characterized by less frequent network reconfigurations while those who did not change their opinions tend to have more flexible brain networks with frequent reconfigurations. The nature of these frequent network configurations suggests a fundamentally different thought process between intervals in which individuals are easily influenced by social media and those in which they are not. We also show that these reconfigurations are distinct to the brain dynamics during an in-person discussion with strangers on the same content. Together, these findings suggest that brain network reconfigurations may not only be diagnostic to the informational context but also the underlying opinion formation.
... Statistical analysis showed that 50 the prefrontal gamma asymmetry was related to willing to 51 pay responses significantly. Then, Golnar-Nik et al. [10] 52 considered the hypothesis if the PSD feature from the EEG 53 is a suitable approach for predicting the customers decision-54 making in a like/dislike task. In the algorithm the features 55 were classified by using Support Vector Machine (SVM) and 56 Linear Discriminant Analysis (LDA). ...
The present study considers the decision making of customers in a like/dislike task with respect to the gender of customers. The investigation is performed by recording electroencephalography (EEG) signal from 20 subjects that stimulated by displaying images of shoes. In the algorithm, the EEG signals were denoised by using artifact subspace reconstruction and independent component analysis methods. The Wavelet technique is then applied to attain five EEG frequency bands and, subsequently linear and nonlinear features were extracted. The extracted features includes linear features, namely the power spectral density and energy of wavelet; and nonlinear features, namely the fractal dimension, entropy, and trajectory volume behavior quantifiers. The meaningfulness of the features for identifying discriminative channels as well as frequency bands is considered by means of Wilcoxon Rank Sum statistical test. The identifications of Like/Dislike conditions were then facilitated by the Support Vector Machine, Random Forest (RF), Linear Discriminant Analysis, and K-Nearest Neighbors classifiers. Results illustrated that higher frequency bands, the combination of theta, alpha, and beta, in Fp1, Fp2, F7, F8, Cz, and Pz regions was observed for female group. The most distinctive feature and classifier for the female group was the energy of the wavelet coefficient and RF classifier, respectively, that produced the highest accuracy rate of 71.51 ± 5.1%. In addition, the most distinctive features for males were sample and approximate entropy, as well as the Higuchi fractal dimension that with the RF classifier produced an accuracy rate of 71.33 ± 14.07%. The linear features investigation revealed more involved brain regions in a like/dislike task than the previous studies. In addition, it is revealed that the Like decision-making happens earlier than Dislike.
... Statistical analysis showed that 50 the prefrontal gamma asymmetry was related to willing to 51 pay responses significantly. Then, Golnar-Nik et al. [10] 52 considered the hypothesis if the PSD feature from the EEG 53 is a suitable approach for predicting the customers decision-54 making in a like/dislike task. In the algorithm the features 55 were classified by using Support Vector Machine (SVM) and 56 Linear Discriminant Analysis (LDA). ...
The present study considers the decision making of customers in a like/dislike task with respect to the gender of customers. The investigation is performed by recording electroencephalography (EEG) signal from 20 subjects that stimulated by displaying images of shoes. In the algorithm, the EEG signals were denoised by using artifact subspace reconstruction and independent component analysis methods. The Wavelet technique is then applied to attain five EEG frequency bands and, subsequently linear and nonlinear features were extracted. The extracted features includes linear features, namely the power spectral density and energy of wavelet; and nonlinear features, namely the fractal dimension, entropy, and trajectory volume behavior quantifiers. The meaningfulness of the features for identifying discriminative channels as well as frequency bands is considered by means of Wilcoxon Rank Sum statistical test. The identifications of Like/Dislike conditions were then facilitated by the Support Vector Machine, Random Forest (RF), Linear Discriminant Analysis, and K-Nearest Neighbors classifiers. Results illustrated that higher frequency bands, the combination of theta, alpha, and beta, in Fp1, Fp2, F7, F8, Cz, and Pz regions was observed for female group. The most distinctive feature and classifier for the female group was the energy of the wavelet coefficient and RF classifier, respectively, that produced the highest accuracy rate of 71.51 ± 5.1%. In addition, the most distinctive features for males were sample and approximate entropy, as well as the Higuchi fractal dimension that with the RF classifier produced an accuracy rate of 71.33 ± 14.07%. The linear features investigation revealed more involved brain regions in a like/dislike task than the previous studies. In addition, it is revealed that the Like decision-making happens earlier than Dislike.
... From EEG power, Golnar-Nik located the frontal and Centro-parietal locations (Figure 1.4.2) to be the most critical in predicting the "like" and "dislike" decisions of consumers. In the end, they achieved an 87% in predicting consumer decision-making incidence [55]. ...
This paper addresses both the various EEG applications and the current EEG market ecosystem propelled by machine learning. Increasingly available open medical and health datasets using EEG encourage data-driven research with a promise of improving neurology for patient care through knowledge discovery and machine learning data science algorithm development. This effort leads to various kinds of EEG developments and currently forms a new EEG market. This paper attempts to do a comprehensive survey on the EEG market and covers the six significant applications of EEG, including diagnosis/screening, drug development, neuromarketing, daily health, metaverse, and age/disability assistance. The highlight of this survey is on the compare and contrast between the research field and the business market. Our survey points out the current limitations of EEG and indicates the future direction of research and business opportunity for every EEG application listed above. Based on our survey, more research on machine learning-based EEG applications will lead to a more robust EEG-related market. More companies will use the research technology and apply it to real-life settings. As the EEG-related market grows, the EEG-related devices will collect more EEG data, and there will be more EEG data available for researchers to use in their study, coming back as a virtuous cycle. Our market analysis indicates that research related to the use of EEG data and machine learning in the six applications listed above points toward a clear trend in the growth and development of the EEG ecosystem and machine learning world.
... First, from a theoretical perspective, the first contribution (Byrne) describes a pendulum-like approach to neuron interactions: this involves the rapid firing and restarting of the process and the clusters of neurons in circuits. A digital analogy is proposed through electroencephalogram (EEG) techniques to show frequency changes that are characteristic of different cognitive processes (Golnar-Nik et al., 2019). In this way, each pendulum would represent a process in terms of length, weight, and a damping factor, previously described in terms of quantum search (Chen and Brylinski, 2002). ...
The world we live in is drastically different from previous decades in terms of digitalization expansion. Logically, one might expect that these changes modify the way we behave, our habits, the way we do tasks, communicate and access information (Moret-Tatay et al., 2018; Wang et al., 2021). And, therefore, the functioning of the brain and even its anatomy. If so, it seems imperative to examine how it affects our cognition. Furthermore, based on the assumption that the brain is plastic and adaptable, some alterations and changes are expected to optimize resources or even
compensations by improving other skills (Oliveira et al., 2018; Bubbico et al., 2020; Della Gatta et al., 2021).
... Researches in consumer neuroscience has demonstrated, EEG recordings of consumers emotional states can provide highly correlated information regarding their product preferences and liking. Such knowledge describing consumer behavior is now being portrayed as a decisive aspect for contemporary marketing schemes [57], [74], [90]. ...
The major challenge in any electroencephalogram (EEG) classi_cation task lies with in the dilemma of feature extraction, as raw time series signal provide little correlated information, yet it holds colossal varieties of hidden feature patterns. Frequency domain transformations are considered state of the art to tackle such complexity. Nevertheless such conventional feature extraction techniques for instance; discrete wavelet transformation (DWT), short time Fourier transform (STFT), di_erential entropy or classical non-parametric power spectral density (PSD) estimation models are computationally expensive as they demonstrate high computational complexity and extensive run time. Consequently arti_cial intelligence driven multi channel EEG based systems struggles to process neural information in real time and such barrier minimizes the dynamics of relevant human computer interaction (HCI) and brain computer interaction (BCI) applications. Multiple Signal Classi_cation Algorithm (MUSIC) is an eigen decomposition based parametric PSD estimation model, which solely uses linear transformation rather than computing windowed periodigram from autocorrelted function of the targeted signal for transformation. Hence MUSIC algorithm should demonstrate lesser time complexity and run time than contemporary classical non-parametric PSD models. Nevertheless this particular model is relatively unexplored for such feature extraction task speci_cally in the area of emotion recognition, as the model is di_cult to implement in terms of EEG signals which demonstrate random behaviour. Our research investigates the performance of MUSIC algorithm in feature extraction task for emotion recognition from multi channel EEG signals and compares its performance with conventional classical non parametric models. It also clarifies the complexity in subspace estimations for EEG waveforms through in detailed analysis, which are indispensable parameters for implementing any eigen decomposition based models in such particular cases. Our proposed model derived state of the art 5-fold cross validation accuracies of 97% and 97:4% for Multi Layer Perceptron (MLP) network and Hybrid Long Term Short Memory (LSTM)-MLP network, respectively on the SEED emotional dataset. The proposed MUSIC model optimizes 95% to 96% run time comparing with conventional classical non-parametric techniques for feature extraction. With exceptional 0:01 sec. (machine specific) run time for feature extraction task, the proposed model shows great prospect in real time applications. Network performance and advanced visualization techniques demonstrate the MUSIC model based feature space holds significant superiority over non-parametric model generated feature space. Additionally the research also found extensive flaws in the widely popular SEED dataset, which were ignored in previously. Over 17% trials were found to hold multiple corrupt channel resulted from external artifacts, which should have effected previously conducted researches. Our research also discusses the effects of such awed trial in network performance.
... Neuromarketing is a novel method of consumer-oriented product sales that utilizes non-invasive brain signal recording techniques, such as electroencephalography (EEG), functional magnetic resonance imaging (fMRI), functional near-infrared spectroscopy (fNIRS), positron emission tomography (PET), etc, to measure the consumer's brain response to marketing stimuli and latter build up new sales strategies. As pointed out in previous studies [6,7], benefits such as low cost and high temporal resolution has brought EEG to the forefront of scientific research in this field [8][9][10]. ...
In this work, EEG signals that are showing the frequency of the power bands were examined by wavelet power spectrum through neuromarketing outline in order to predict purchaser appetites while they look E-commerce goods. When extraction of these power bands, fixed overlap segment and 3 different sample lengths were counted in sliding window technique. k-NN was implemented for evaluating classification accuracy. The best result, 70.24% k-NN accuracy was obtained for 2-seconds sample length.
... Summary of the citations in various areas in shown below (see Table 2). (2015) 2007), Barch (2006), Lawrence et al. (2006), Singer (2006), Grosbras and Paus (2006), Kalisch et al. (2005), Lewis andPhil (2004), De Gelder et al. (2004), Wicker et al. (2003), Ochsner et al. (2002), Seifritz et al. (2003), Sprengelmeyer et al. (1998) EEG Agarwal and Xavier (2015), Astolfi et al. (2009), Balconi et al. (2014, Boldo et al. (2015), Berčík et al. (2015Berčík et al. ( , 2016, Brown et al. (2012), Custdio (2010), Dapkevicius andMelnikas (2009), Dimpfel (2015), Golnar-Nik et al. (2019), Kumar and Singh (2015), Lewis and Phil (2004) (2006), Dimberg et al. (2000), Harford (2003), Lanzetta and Englis (1989), Stein et al. (2002) ...
Little work has been done to bring these various studies together to offer a solid building block on which theory can be further developed in a rigorous manner. We address this gap by conducting a multidisciplinary literature review to provide a unified understanding of research that has been conducted within neuromarketing. Articles related to neuroscience in the past 20 years (1997-2016) were downloaded from EBSCO using relevant keywords. Contribution wise, first, we provide a unified perspective of the research that's been conducted within this field. Second, we identify future research opportunities within neuromarketing, such as the tools that can be employed, specific areas where the tools can be applied, and specific emotions that can be investigated. Third, we provide additional guidance by discussing strengths and drawbacks associated with these various approaches. This article segregates past research in three unique buckets: tools, areas in marketing and emotions.
... Autoregressive modeling of action potentials is another way for spike sorting [12]. Furthermore, the power spectrum density function of biological data might contain useful information for activity discrimination [13]. ...
Analysis of neuronal activities is essential in studying nervous system mechanisms. True interpretation of such mechanisms relies on detecting and sorting neuronal activities, which appear as action potentials or spikes in the recorded neural data. So far, several algorithms have been developed for spike sorting. In this paper, spike sorting was addressed using entropy measures. A method based on a modified version of approximate entropy was proposed for feature extraction, which captured the local variations in spike waveforms as well as global variation to create the feature space. Results showed that the entropy-based feature extraction method created more distinguishing features, which reduces spike sorting errors. The proposed method was capable of separate different spikes in small-scale structures, where the technique such as principal component analysis fails.
... (VS). These regions, within social contexts, have been linked to susceptibility to social influence from peers (for a comprehensive review see Falk & Scholz, 2018). More broadly in EEG, several frequency bands have been implicated in decision making (e.g., Nakao et. al. 2019), but often show some specificity in frontal and parietal regions (e.g., Golnar-Nik et. al. 2019). Interestingly, a recent study inspecting long range temporal correlations (LRTC) in EEG recordings has shown a relationship between theta to alpha bands and the abstract concept of self identity and identity confusion (Sugimura et. al. 2021). Due to the highly complex decision, speculation, and potential action, our results most likely ...
Since their development, social media has grown as a source of information and has a significant impact on opinion formation. Individuals interact with others and content via social media platforms in a variety of ways but it remains unclear how decision making and associated neural processes are impacted by the online sharing of informational content, from factual to fabricated. Here, we use EEG to estimate dynamic reconfigurations of brain networks and probe the neural changes underlying opinion change (or formation) within individuals interacting with a simulated social media platform. Our findings indicate that the individuals who show more malleable opinions are characterized by less frequent network reconfigurations while those with more rigid opinions tend to have more flexible brain networks with frequent reconfigurations. The nature of these frequent network configurations suggests a fundamentally different thought process between the individuals who are more easily influenced by social media and those who are not. We also show that these reconfigurations are distinct to the brain dynamics during an in-person discussion with strangers on the same content. Together, these findings suggest that network reconfigurations in the brain may not only be diagnostic to the informational context but also the underlie opinion formation.
Author Summary
Distinctive neural underpinnings of opinion formation and change during in-person and online social interactions are not well understood. Here, we analyze EEG recordings of the participants interacting with a simulated social media platform and during an in-person discussion using a network-based analysis approach. We show that the structure of network reconfigurations during these interactions is diagnostic of the opinion change and the context in which information was received.
The aperture between the marketing domain and the electroencephalography (EEG)‐based brain–computer interface (BCI) has been achieved with the inception of neuromarketing. This domain helps access the hidden information of the preferences and tastes of the consumers who intend to purchase. Research scholars have experimented with this emerging area in multiple aspects like designing pricing, promotions, predicting purchase‐related activities, new product development, and so on. In this study, we have proposed an innovative use of neuromarketing to build a recommendation system. This recommendation system can potentially suggest suitable products to the consumer based on the past purchase behavior. This proposal carries huge potential in converting visitors to shoppers, increasing average order value, increasing the number of items per order, designing personalized promotions, and so on. The commonality of activated brain signals has been used to build this recommendation system. This neuromarketing‐based recommendation system carries the advantage over the traditional recommendation system as this system suggests products based on the actual real‐time state of the brain during the purchase. This system successfully initiated the starting point of building a neuromarketing‐based recommendation system.
Recent technological and methodological advances have led to the possibility of a wider range of data being incorporated into travel choice models. In particular, physiological data such as eye-tracking information, skin conductance, heart rate recordings and electroencephalogram (EEG) have emerged as promising sources of information that could be used to gain insights into the decision-making process as well as the decision-maker's state of mind. However, research on methodologies to utilise these data sources and to integrate them with mobility data for advancing state-of-the-art travel behaviour models is still very limited. In this paper, we discuss the key benefits of using these emerging sources of physiological data, review applications of different types of physiological data and highlight their strengths and weaknesses. Particular attention is paid to two different generic frameworks for integrating these types of data into econometric choice models of travel behaviour. The first framework involves using physiological sensor data as indicators of latent variables while in the second framework, they are used as exogenous variables. We identify the research gaps and outline the directions for future methodological and applied research required to better utilise the physiological data for travel choice models.
The popular brain monitoring method of electroencephalography (EEG) has seen a surge in commercial attention in recent years, focusing mostly on hardware miniaturisation. This has led to a varied landscape of portable EEG devices with wireless capability, allowing them to be used by relatively unconstrained users in real-life conditions outside of the laboratory. The wide availability and relative affordability of these devices provide a low entry threshold for newcomers to the field of EEG research. The large device variety and the at times opaque communication from their manufacturers, however, can make it difficult to obtain an overview of this hardware landscape. Similarly, given the breadth of existing (wireless) EEG knowledge and research, it can be challenging to get started with novel ideas. Therefore, this paper first provides a list of 44 wireless EEG devices along with a number of important—sometimes difficult-to-obtain—features and characteristics to enable their side-by-side comparison, along with a brief introduction to each of these aspects and how they may influence one's decision. Secondly, we have surveyed previous literature and focused on 110 high-impact journal publications making use of wireless EEG, which we categorised by application and analysed for device used, number of channels, sample size, and participant mobility. Together, these provide a basis for informed decision making with respect to hardware and experimental precedents when considering new, wireless EEG devices and research. At the same time, this paper provides background material and commentary with respect to pitfalls and caveats regarding this increasingly accessible line of research.
Recent years have been characterized by an enormous technological progress, also with reference to specific sectors like healthcare. In this respect, a peculiar interest has recently concerned new instruments adopted in neuroscientific activities, leading the scientific community to improve the field of neurosciences, with substantial developments specifically in neurological research, starting a new season of studies concerning particularly the emotional component of the consumer behaviour, arriving at neuromarketing. However, despite the increasing interest that this new field of study has recently assumed, there still remain several issues of ethical nature. This study, after a brief introduction to neuromarketing research, is focused on the theme of its ethical sustainability, proposing some possible solutions, with specific examples in the field of wine marketing.
Tourism is an industry that generally shows marketing activities based on intangible concepts. However, traditional marketing research is built on answers given by consumers at the level of consciousness, and this may lead to misleading results. Neuromarketing allows consumers to be evaluated from an expanded and high-accuracy perspective. This study aims to evaluate the application areas of electroencephalography (EEG) and functional near-infrared spectroscopy (fNIRS) methods, which provide data for the measurement of cognitive activities in neuromarketing research in the field of tourism. In this study, data were reported using descriptive analysis, which is one of the qualitative research methods. According to the results of the research, although the number of EEG
and fNIRS methods has increased in marketing studies, they are extremely less preferred in the field of tourism due to constraints such as high cost, time, and place limitation. However, the increase in neuromarketing studies in the field of tourism can provide a broad perspective on marketing activities and consumers.
Selama bertahun-tahun, industri ritel bahan makanan telah tumbuh pada tingkat yang fenomenal. Akibatnya, industri ritel didorong untuk meningkatkan pendekatan pemasaran ke Neuromarketing untuk memperkuat keterlibatan pelanggan dan meningkatkan penjualan. Saat ini, pengetahuan menyeluruh tentang cara kerja otak sangat penting, serta bagaimana fungsi otak yang canggih menghasilkan perilaku manusia yang sangat terspesialisasi. Disertasi akan menguji seberapa efektif Neuromarketing dalam mempengaruhi keputusan pembelian konsumen di toko ritel grosir dan seberapa penting dampaknya terhadap penjualan. Dengan demikian, tujuan dari disertasi ini adalah untuk menemukan alat Neuromarketing yang dapat digunakan untuk mempengaruhi keputusan pembelian pelanggan dan meningkatkan penjualan. Akhirnya, disertasi menyimpulkan bahwa neuromarketing yang menggabungkan merchandising sensorik (pencahayaan, warna, musik, aroma, & suhu) akan cenderung memiliki pengaruh yang menguntungkan pada keputusan pembelian konsumen dan meningkatkan penjualan, jika pengecer dapat mempertimbangkan faktor budaya, iklim kondisi area tertentu, preferensi pelanggan, budaya etis, serta efisiensi alokasi produk dan tata letak toko dan yang paling penting, faktor dan pertimbangan tersebut dapat diterapkan dengan tepat oleh pengecer.
Objective: The role of unconscious factors in the motivational process, both in decision making and education, has increasingly been noticed. The effectiveness of commercial, non-commercial, and educational messages, as well as the structures of educational advertisements in neuromarketing, are discussed because the advertising audience has complex emotions. It is essential to determine the impact of motivational factors. In this regard, the present systematic review study was conducted to identify motivational factors related to the effectiveness of neuromarketing advertising. Methods: In this systematic review study, English full-text articles published on Science Direct, Scopus, Google Scholar, and Emerald databases from 1990 to 2021 were searched with the keywords of “neuromarketing”, “commercial and non-commercial messages”, “ad effectiveness”, “behavior”, and “attitude”. Afterward, the qualified articles were reviewed. Results: Firstly, 900 articles were identified from different databases, of which 300 met the inclusion criteria. A total of 210 articles were deleted because they lacked experimental studies. Accordingly, 90 articles were selected for the final review. In total, 37 factors were identified related to the effectiveness of advertising in neuromarketing. Conclusion: The two most influential factors in the research were attention and positive emotions. The greatest emphasis on the effectiveness of advertising is on attention-drawing stimuli and provoking positive emotions. Memory and negative emotions were identified as two other critical factors among the influencing factors.
Electroencephalography (EEG) offers insights into the neural responses of consumers to marketing stimuli which can be used to evaluate the effectiveness of such stimuli in directing favorable consumer behavior in real-world settings. This practical review provides guidelines for using EEG in consumer and marketing research. It provides recommendations for how EEG may effectively be employed in neuromarketing in the future. EEG requires careful experimental design, and as such, we outline current recommendations for the collection, processing, analysis, and interpretation of EEG evoked potentials (i.e., event related potentials; ERP) and spectral content (i.e., EEG frequency). By providing an introductory overview of EEG measures in marketing and consumer research, this practical review extends previous literature that is primarily focused on other neuroimaging techniques (e.g., functional magnetic resonance imaging) and other disciplines (e.g., economics and organizational behaviour). Furthermore, by reviewing how EEG has been used throughout psychophysiological and neuromarketing research, we provide recommendations on how EEG can be used to measure marketing-related outcomes. These include processes relating to perception, attention, memory, emotion, and cognitive load, demonstrating the unique value of considering the neural responses captured by EEG in understanding and predicting consumer behavior.
The research aims to enhance marketing capabilities of companies through their commitment to social responsibility, which will bring company many benefits, most notably application of neuromarketing, social responsibility in its economic, social and environmental dimensions with dependent variable of neuromarketing. A research reached a set of conclusions, the most prominent of which is that customers are involuntarily affected by company's commitment to social responsibility, and therefore a company can use this in application of neuromarketing to ensure increased sales of its product mix and achieve high loyalty to existing customers in the markets.
Environmental stimuli can have a significant impact on our decisions. Elements of the store atmosphere, such as music, lights and smells, all have effects on choices, but these have been only vaguely investigated. In the present study, we aim to uncover the effect of strawberry scent on the gazing behavior and choices of the 62 recruited participants. A static eye-tracker was used to study the effect of scent, released by a diffuser. In total, 31 participants completed the study under odorless conditions, while another 31 participants had strawberry fragrance sprayed into the air. The objectives of the study were (1) to determine whether the most gazed-upon product in each of the four categories (chocolate, tea, muesli bar, yoghurt) was chosen, (2) whether the presence of the strawberry scent influenced consumer decision making, i.e., whether the strawberry scent influenced more people to choose strawberry-flavored products, and (3) to introduce the application of a fast and easy-to-use technique for the qualitative analysis of strawberry aroma present in the air during eye-tracking measurements. The results show that (1) participants chose the product they had studied the longest, for all four categories, and (2) the presence or absence of the scent had no significant effect on choice, with the same frequencies of choosing each product in the two conditions regardless of the flavor of the products.
Purpose: The main objective of this article examines the differences in the switching behaviours of older (Generation X and baby boomers) and younger (Millennials and Generation Z) customers of banks in the UK, with emphasis is on online and offline banking services. The secondary objective is also to evaluate the influential factors of customer satisfaction in the banking sector relating to customer switching behaviours. Design and Methodology: The study adopts a positivist approach by using questionnaire survey to gather data from 106 clients of banks in the UK. The questionnaire comprises 30 questions, the majority of which use a 5-point Likert scale. Findings: No statistically significant difference is found when the switching behaviours of older and younger customers are compared. However, younger customers perceived online banking as more useful due to their greater ease-of-use. Critically, security and ease-of-use are the most noticeable determinants of customer satisfaction influencing switching behaviours. Research Limitations: This study lacks a managerial perspective on switching behaviours. This means that the applicability of the findings to banks in the UK is limited, suggesting that a further exploration in that respect may be warranted. Practical Implications: Banks in the UK are urged to re-focus their customer relationship strategies to improve security and perceived ease-of-use. It is also recommended that marketing campaigns are launched to inform the clients about the benefits of online banking with a specific emphasis on perceived usefulness and ease-of-use.
The payment method is a key factor influencing online consumers’ purchase decisions. However, little is known about its underlying neural basis, which could help to reveal the mechanism by which payment methods affect online purchase decisions. Combined with the event-related potentials (ERPs), a neuroscience technique with the advantage of measuring implicit psychological variables to reveal the mechanism behind behaviors, this study uncovers consumers’ discrepant perception between pay-online and pay-on-delivery in different purchase contexts through an online purchase task. Behavioral results showed that purchase intention is higher for pay-online than pay-on-delivery, regardless of product type. At the brain level, we found consumers induce higher perceived risk (indicated in larger N2 amplitudes) and smaller negative emotion (mirrored by larger P3 amplitudes) for pay-online than pay-on-delivery, especially when shopping for search products. However, this effect disappeared when purchasing experience products. Moreover, the larger perceived risk for experience products than search products may lead consumers to ignore the difference between the two payment methods. This study helps online sellers optimize payment services for specific products.
Exploiting biometric measures, especially neurophysiological data of evaluator for product evaluation is advantageous at avoiding bias and subjectivity in expert scoring process. This paper proposes an approach that integrates electroencephalograph (EEG) and eye-tracking (ET) data in a new way to derive multi-faceted supportive information for product evaluation. Firstly, emotion recognition from EEG signals of evaluator is carried out with a spatial–temporal neural network. Then, based on correlations between emotions and preferential judgement, general customer preference toward product design scheme is inferred from emotions by fuzzy system. Finally, general preference is integrated with ET data at application-level to quantify fine-grained customer preferences toward design modules and visual attractiveness. This approach is verified with a case study which evaluates six designs of frontal area of automotive interior, and valuable supportive information for design decision-making is yielded. Also, comprehensive analysis is conducted and the results verify the effectiveness of proposed approach.
The aim of the study within this framework, articles published on Neuromarketing between 2013 and 2018 were examined separately in the title and topic categories on the Web of Science (WOS) site and is aimed to analyse the status of the articles in the last 5 years. Articles with desired properties are parsed and internationally published articles have been reached. Before data is thrown into the CiteSpace V program, the implementation steps you will see on the next pages have been implemented individually. Bibliometric analysis was evaluated as visual, textual and statistical analysis by filling the relevant fields according to the results of the desired analysis. According to the number of citations in the studies, the author's name, country analysis of articles, institutions where authors work, article page numbers, publication type, research area, year of publication of articles, source title, and document type were examined.
Consumers frequently make decisions about how much they are willing to pay (WTP) for specific products and services, but little is known about the neural mechanisms underlying such calculations. In this study, we were interested in testing whether specific brain activation—the asymmetry in engagement of the prefrontal cortex—would be related to consumer choice. Subjects saw products and subsequently decided how much they were willing to pay for each product, while undergoing neuroimaging using electroencephalography. Our results demonstrate that prefrontal asymmetry in the gamma frequency band, and a trend in the beta frequency band that was recorded during product viewing was significantly related to subsequent WTP responses. Frontal asymmetry in the alpha band was not related to WTP decisions. Besides suggesting separate neuropsychological mechanisms of consumer choice, we find that one specific measure—the prefrontal gamma asymmetry—was most strongly related to WTP responses, and was most coupled to the actual decision phase. These findings are discussed in light of the psychology of WTP calculations, and in relation to the recent emergence of consumer neuroscience and neuromarketing.
Due to the limitations of the human ability to process information, e-consumers’ decisions are likely to be influenced by various cognitive biases, such as the attribute framing effect. This effect has been well studied by numerous scholars; however, the associated underlying neural mechanisms with a critical temporal resolution have not been revealed. Thus, this study applies the measurement of event-related potentials (ERPs) to directly examine the role of attribute framing in information processing and decision-making in online shopping. The behavioral results showed that participants demonstrated a higher purchase intention with a shorter reaction time under a positive framing condition compared to participants under a negative framing condition. Compared with positive framing messages, the results of ERPs indicated that negative framing messages attracted more attention resources at the early stage of rapid automatic processing (larger P2 amplitude) and resulted in greater cognitive conflict and decision difficulty (larger P2-N2 complex). Moreover, compared with negative messages, positive framing messages allowed consumers to perceive a better future performance of products and classify these products as a categorization of higher evaluation (larger LPP amplitude) at the late cognitive processing stage of evaluation. Based on these results, we provide evidence for a better understanding of how different attribute framing messages are processed and ultimately lead to the framing effect.
Marketing and promotions of various consumer products through advertisement campaign is a well known practice to increase the sales and awareness amongst the consumers. This essentially leads to increase in profit to a manufacturing unit. Re-production of products usually depends on the various facts including consumption in the market, reviewer’s comments, ratings, etc. However, knowing consumer preference for decision making and behavior prediction for effective utilization of a product using unconscious processes is called “Neuromarketing”. This field is emerging fast due to its inherent potential. Therefore, research work in this direction is highly demanded, yet not reached a satisfactory level. In this paper, we propose a predictive modeling framework to understand consumer choice towards E-commerce products in terms of “likes” and “dislikes” by analyzing EEG signals. The EEG signals of volunteers with varying age and gender were recorded while they browsed through various consumer products. The experiments were performed on the dataset comprised of various consumer products. The accuracy of choice prediction was recorded using a user-independent testing approach with the help of Hidden Markov Model (HMM) classifier. We have observed that the prediction results are promising and the framework can be used for better business model.
Recently, elevated ongoing pre-stimulus beta power (13–17 Hz) at encoding has been associated with subsequent memory formation for visual stimulus material. It is unclear whether this activity is merely specific to visual processing or whether it reflects a state facilitating general memory formation, independent of stimulus modality. To answer that question, the present study investigated the relationship between neural pre-stimulus oscillations and verbal memory formation in different sensory modalities. For that purpose, a within-subject design was employed to explore differences between successful and failed memory formation in the visual and auditory modality. Furthermore, associative memory was addressed by presenting the stimuli in combination with background images. Results revealed that similar EEG activity in the low beta frequency range (13–17 Hz) is associated with subsequent memory success, independent of stimulus modality. Elevated power prior to stimulus onset differentiated successful from failed memory formation. In contrast, differential effects between modalities were found in the theta band (3–7 Hz), with an increased oscillatory activity before the onset of later remembered visually presented words. In addition, pre-stimulus theta power dissociated between successful and failed encoding of associated context, independent of the stimulus modality of the item itself. We therefore suggest that increased ongoing low beta activity reflects a memory promoting state, which is likely to be moderated by modality-independent attentional or inhibitory processes, whereas high ongoing theta power is suggested as an indicator of the enhanced binding of incoming interlinked information.
This research aims to test whether product's packaging color influences customers' purchasing preferences or not and does time pressure moderates this relationship? It studied the importance of color in determining customers' buying preferences when they have limited time to do shopping. This study revealed that buying preference of a customer is relatively more dependent on the color scheme than on time constraint. However, time pressure was an important moderating factor which influenced the effect of packaging colors on customers' purchasing preferences. This study emphasized that companies cannot afford to ignore the significance of time constraints and color scheme of the products on customers' buying behavior.
To cite:
Javed, S. A. & Javed, S. (2015). The impact of product’s packaging color on customers’ buying preferences under time pressure. Marketing and Branding Research, 2(1), 4-14.
This article investigates the potential use of Eye Tracking as a neuromarketing tool and its potential for marketing in general. We sought to identify some of the main applications within the mainstream of marketing. The objective of this research was achieved by means of a conceptual literature review. The results of our research indicate important potential uses for Eye Tracking in practical marketing applications, such as brand equity, segmentation, new product development, pricing decisions, place decisions, promotion decisions, and social marketing studies. It is believed that in the near future, neuromarketing tools such as Eye Tracking will be part of mainstream marketing studies.
Every year, retailers launch a myriad of new products. The success rate of such new products directly influences a retailer's success in terms of gross profit, customer loyalty and brand image. In the past decades, many self-report and focus group based methods were implemented to gain insights in future market performance of new products. However, social psychology and market research studies have established that self-reports are unreliable to accurately predict customer preference. In this article, we propose a novel approach based on brain data to forecast product performance and discuss the importance of pre-market forecasting in the footwear retailing industry. We implemented and validated the tool in collaboration with a European shoe store chain. This case study showed that self-report based methods cannot accurately foretell success, while using brain data the prediction accuracy reached 80 per cent. We also compared how these two different methods might influence company gross profit. Simulations based on sales data showed that self-report based prediction would lead to a 12.1 per cent profit growth, while brain scan based prediction would increase profit by 36.4 per cent. Thus, this innovative neuroscientific approach greatly improves brand image and brings considerable value for organizations, shareholders as well as consumers.
Eye-tracking has been extensively used to quantify audience preferences in the context of marketing and advertising research, primarily in methodologies involving static images or stimuli (i.e., advertising, shelf testing, and website usability). However, these methodologies do not generalize to narrative-based video stimuli where a specific storyline is meant to be communicated to the audience. In this paper, a novel metric based on eye-gaze dispersion (both within and across viewings) that quantifies the impact of narrative-based video stimuli to the preferences of large audiences is presented. The metric is validated in predicting the performance of video advertisements aired during the 2014 Super Bowl final. In particular, the metric is shown to explain 70% of the variance in likeability scores of the 2014 Super Bowl ads as measured by the USA TODAY Ad-Meter. In addition, by comparing the proposed metric with Heart Rate Variability (HRV) indices, we have associated the metric with biological processes relating to attention allocation. The underlying idea behind the proposed metric suggests a shift in perspective when it comes to evaluating narrative-based video stimuli. In particular, it suggests that audience preferences on video are modulated by the level of viewers lack of attention allocation. The proposed metric can be calculated on any narrative-based video stimuli (i.e., movie, narrative content, emotional content, etc.), and thus has the potential to facilitate the use of such stimuli in several contexts: prediction of audience preferences of movies, quantitative assessment of entertainment pieces, prediction of the impact of movie trailers, identification of group, and individual differences in the study of attention-deficit disorders, and the study of desensitization to media violence.
Significance
We show that the functional coordination between the two hemispheres of the brain is maintained by strong and stable interactions of a specific subset of connections between homotopic regions. Our data suggest that the stability of those functional interactions is mediated in part by the direct anatomical projections of large, highly myelinated fibers that traverse the corpus callosum. These functional properties were evident in both humans and macaques, suggesting a preserved framework for interhemispheric communication despite an increase in functional lateralization in humans. These results contribute to our fundamental understanding of how dynamic functional interactions between the two hemispheres of the mammalian brain are supported by its underlying anatomical architecture.
We analyzed skin conductance response (SCR) as a psychophysiological index to evaluate affective aspects of consumer preferences for cosmetic products. To examine the test-retest reliability of association between preferences and SCR, we asked 33 female volunteers to complete two experimental sessions approximately 1 year apart. The participants indicated their preferences in a typical paired comparison task by choosing the better option from a combination of two products among four products. We measured anticipatory SCR prior to expressions of the preferences. We found that the mean amplitude of the SCR elicited by the preferred products was significantly larger than that elicited by the non-preferred products. The participants' preferences and corresponding SCR patterns were well preserved at the second session 1 year later. Our results supported cumulating findings that SCR is a useful index of consumer preferences that has future potential, both in laboratory and marketing settings.
It is now established that neural imaging technology can predict preferences over consumer products. However the applicability of this method to consumer marketing research remains in question, partly because of the expense required. In this article, we demonstrate that neural measurements made with a relatively low-cost and widely available measurement method — Electroencephalogram (EEG) — can predict future choices over consumer products. In our experiment, subjects viewed individual consumer products in isolation, without making any actual choices, while we measured their neural activity with EEG. After these measurements were taken, subjects then made choices between pairs of the same products. We find that neural activity measured from a mid-frontal electrode displays an increase in the N200 component and a weaker theta band power that correlates with a more preferred good. Using state-of-the-art techniques for relating neural measurements to choice prediction, we demonstrate that these measures predict subsequent choices. Moreover, the accuracy of prediction depends on both the ordinal and cardinal distance of the EEG data: the larger the difference in EEG activity between two goods, the better the predictive accuracy.
While there is a growing body of functional magnetic resonance imaging (fMRI) evidence implicating a corpus of brain regions in value-based decision-making in humans, the limited temporal resolution of fMRI cannot address the relative temporal precedence of different brain regions in decision-making. To address this question, we adopted a computational model-based approach to electroencephalography (EEG) data acquired during a simple binary choice task. fMRI data were also acquired from the same participants for source localization. Post-decision value signals emerged 200 ms post-stimulus in a predominantly posterior source in the vicinity of the intraparietal sulcus and posterior temporal lobe cortex, alongside a weaker anterior locus. The signal then shifted to a predominantly anterior locus 850 ms following the trial onset, localized to the ventromedial prefrontal cortex and lateral prefrontal cortex. Comparison signals between unchosen and chosen options emerged late in the trial at 1050 ms in dorsomedial prefrontal cortex, suggesting that such comparison signals may not be directly associated with the decision itself but rather may play a role in post-decision action selection. Taken together, these results provide us new insights into the temporal dynamics of decision-making in the brain, suggesting that for a simple binary choice task, decisions may be encoded predominantly in posterior areas such as intraparietal sulcus, before shifting anteriorly.
The electrophysiology technique now provides an alternative way to evaluate users' emotional states in real time, but how to confirm the valence of emotions using these techniques is still a concern to researchers. Frontal alpha asymmetry (FAA) is often used as an index of pleasantness or liking in neuro-marketing, but results in related fields are not consistent. In this study, we in-vestigated the emotional states of users interacting with mobile phone applica-tions (APPs) using FAA. Twenty participants participated in this experiment. They were asked to complete several tasks in a scene of everyday life using three APPs of the same type. EEG data and subjective evaluations were record-ed during the experiment. The FAA results showed a positive trend when using an APP that provided an excellent user experience. The mechanism of emotion-al change during interacting with mobile applications and the implications of this research are also discussed in this study.
We present a psychophysiological study of facial expressions of happiness (FEH) produced by advertisements using the FaceReader system (Noldus, 2013) for automatic analysis of facial expressions of basic emotions (FEBE; Ekman, 1972). FaceReader scores were associated with self-reports of the advertisement’s effectiveness. Building on work describing the role of emotions in marketing research, we examined the relationship between the patterns of the FEBE and the perceived amusement of the advertisements, attitude toward the advertisement (AAD) and attitude toward the brand (AB). Differences were observed between FEH scores in response to high-, medium-, and low-amusing video advertisements (AVAs). Positive correlations were found between FEH and AAD and FEH and AB in high- and medium- but not in low-AVAs. As hypothesized, other basic emotions (sadness, anger, surprise, fear, and disgust) did not predict advertisement amusement or advertisements’ effectiveness. FaceReader enabled a detailed analysis of more than 120,000 frames of video-recordings contributing to an identification of global patterns of facial reactions to amusing persuasive stimuli. For amusing commercials, context-specific FEH features were found to be the major indicators of advertisement effectiveness. The study used video-recordings of participants in their natural environments obtained through a crowd-sourcing platform. The naturalistic design of the study strengthened its ecological validity and demonstrated the robustness of the software algorithms even under austere conditions. Our findings provide first evidence for the applicability of FaceReader methodology in the basic consumer science research. (PsycINFO Database Record (c) 2014 APA, all rights reserved)
This study investigated the functional significance of EEG alpha power increases, a finding that is consistently observed in various memory tasks and specifically during divergent thinking. It was previously shown that alpha power is increased when tasks are performed in mind – e.g., when bottom-up processing is prevented. This study aimed to examine the effect of task-immanent differences in bottom-up processing demands by comparing two divergent thinking tasks, one intrinsically relying on bottom-up processing (sensory-intake task) and one that is not (sensory-independence task). In both tasks, stimuli were masked in half of the trials to establish conditions of higher and lower internal processing demands. In line with the hypotheses, internal processing affected performance and led to increases in alpha power only in the sensory-intake task, whereas the sensory-independence task showed high levels of task-related alpha power in both conditions. Interestingly, conditions involving focused internal attention showed a clear lateralization with higher alpha power in parietal regions of the right hemisphere. Considering evidence from fMRI studies, right-parietal alpha power increases may correspond to a deactivation of the right temporoparietal junction, reflecting an inhibition of the ventral attention network. Inhibition of this region is thought to prevent reorienting to irrelevant stimulation during goal-driven, top-down behavior, which may serve the executive function of task shielding during demanding cognitive tasks such as idea generation and mental imagery.
The aim of the present study was to investigate the role of beta oscillatory responses upon cognitive load in healthy subjects and in subjects with mild cognitive impairment (MCI). The role of beta oscillations upon cognitive stimulation is least studied in comparison to other frequency bands. The study included 17 consecutive patients with MCI (mean age = 70.8 ± 5.6 years) according to Petersen's criteria, and 17 age- and education-matched normal elderly controls (mean age = 68.5 ± 5.5 years). The experiments used a visual oddball paradigm. EEG was recorded at 30 cortical locations. EEG-evoked power, inter-trial phase synchronization, and event-related beta responses filtered in 15-20 Hz were obtained in response to target and non-target stimuli for both groups of subjects. In healthy subjects, EEG-evoked beta power, inter-trial phase synchronization of beta responses and event-related filtered beta responses were significantly higher in responses to target than non-target stimuli (p < 0.05). In MCI patients, there were no differences in evoked beta power between target and non-target stimuli. Furthermore, upon presentation of visual oddball paradigm, occipital electrodes depict higher beta response in comparison to other electrode sites. The increased beta response upon presentation of target stimuli in healthy subjects implies that beta oscillations could shift the system to an attention state, and had important function in cognitive activity. This may, in future, open the way to consider beta activity as an important operator in brain cognitive processes.
Branding and advertising have a powerful effect on both familiarity and preference for products, yet no neuroimaging studies have examined neural response to logos in children. Food advertising is particularly pervasive and effective in manipulating choices in children. The purpose of the present study was to examine how healthy children's brains respond to common food and other logos. A pilot validation study was first conducted with 32 children to select the most culturally familiar logos, and to match food and nonfood logos on valence and intensity. A new sample of 17 healthy weight children were then scanned using functional magnetic resonance imaging (fMRI). Food logos compared to baseline were associated with increased activation in orbitofrontal cortex and inferior prefrontal cortex. Compared to nonfood logos, food logos elicited increased activation in posterior cingulate cortex. Results confirmed that food logos activate some brain regions in children known to be associated with motivation. This marks the first study in children to examine brain responses to culturally familiar logos. Considering the pervasiveness of advertising, research should further investigate how children respond at the neural level to marketing.
Emotions accompany everyone in the daily life, playing a key role in non-verbal communication, and they are essential to the understanding of human behavior. Emotion recognition could be done from the text, speech, facial expression or gesture. In this paper, we concentrate on recognition of “inner” emotions from electroencephalogram (EEG) signals as humans could control their facial expressions or vocal intonation. The need and importance of the automatic emotion recognition from EEG signals has grown with increasing role of brain computer interface applications and development of new forms of human-centric and human-driven interaction with digital media. We propose fractal dimension based algorithm of quantification of basic emotions and describe its implementation as a feedback in 3D virtual environments. The user emotions are recognized and visualized in real time on his/her avatar adding one more so-called “emotion dimension” to human computer interfaces.
The visual complexity of Web pages is much talked about; “complex Web pages are difficult to use,” but often regarded as a subjective decision by the user. This subjective decision is of limited use if we wish to understand the importance of visual complexity, what it means, and how it can be used. We theorize that by understanding a user's visual perception of Web page complexity, we can understand the cognitive effort required for interaction with that page. This is important because by using an easily identifiable measure, such as visual complexity, as an implicit marker of cognitive load, we can design Web pages which are easier to interact with. We have devised an initial empirical experiment, using card sorting and triadic elicitation, to test our theories and assumptions, and have built an initial baseline sequence of 20 Web pages along with a library of qualitative and anecdotal feedback. Using this library, we define visual complexity, ergo perceived interaction complexity, and by taking these pages as “prototypes” and ranking them into a sequence of complexity, we are able to group them into: simple, neutral, and complex. This means we can now work toward a definition of visual complexity as an implicit measure of cognitive load.
The importance of music in our daily life has given rise to an increased number of studies addressing the brain regions involved in its appreciation. Some of these studies controlled only for the familiarity of the stimuli, while others relied on pleasantness ratings, and others still on musical preferences. With a listening test and a functional magnetic resonance imaging (fMRI) experiment, we wished to clarify the role of familiarity in the brain correlates of music appreciation by controlling, in the same study, for both familiarity and musical preferences. First, we conducted a listening test, in which participants rated the familiarity and liking of song excerpts from the pop/rock repertoire, allowing us to select a personalized set of stimuli per subject. Then, we used a passive listening paradigm in fMRI to study music appreciation in a naturalistic condition with increased ecological value. Brain activation data revealed that broad emotion-related limbic and paralimbic regions as well as the reward circuitry were significantly more active for familiar relative to unfamiliar music. Smaller regions in the cingulate cortex and frontal lobe, including the motor cortex and Broca's area, were found to be more active in response to liked music when compared to disliked one. Hence, familiarity seems to be a crucial factor in making the listeners emotionally engaged with music, as revealed by fMRI data.
Human daily behaviors are often affected by subjective preferences. Studies have shown that physical responses are affected by unconscious preferences before conscious decision making. Accordingly, attention-related neural activities could be influenced by unconscious preferences. However, few neurological data exist on the relationship between visual attention and subjective preference. To address this issue, we focused on lateralization during visual attention and investigated the effects of subjective color preferences on visual attention-related brain activities. We recorded electroencephalograph (EEG) data during a preference judgment task that required 19 participants to choose their preferred color from 2 colors simultaneously presented to the right and left hemifields. In addition, to identify oscillatory activity during visual attention, we conducted a control experiment in which the participants focused on either the right or the left color without stating their preference. The EEG results showed enhanced theta (4-6 Hz) and decreased alpha (10-12 Hz) activities in the right and left occipital electrodes when the participants focused on the color in the opposite hemifield. Occipital theta synchronizations also increased contralaterally to the hemifield to which the preferred color was presented, whereas the alpha desynchronizations showed no lateralization. The contralateral occipital theta activity lasted longer than the ipsilateral occipital theta activity. Interestingly, theta lateralization was observed even when the preferred color was presented to the unattended side in the control experiment, revealing the strength of the preference-related theta-modulation effect irrespective of visual attention. These results indicate that subjective preferences modulate visual attention-related brain activities.
The aim of this research is to analyze the changes in the EEG frontal activity during the observation of commercial videoclips. In particular, we aimed to investigate the existence of EEG frontal asymmetries in the distribution of the signals' power spectra related to experienced pleasantness of the video, as explicitly rated by the eleven experimental subjects investigated. In the analyzed population, maps of Power spectral density (PSD) showed an asymmetrical increase of theta and alpha activity related to the observation of pleasant (unpleasant) advertisements in the left (right) hemisphere. A correlation analysis revealed that the increase of PSD at left frontal sites is negatively correlated with the degree of pleasantness perceived. Conversely, the de-synchronization of left alpha frontal activity is positively correlated with judgments of high pleasantness. Moreover, our data presented an increase of PSD related to the observation of unpleasant commercials, which resulted higher with respect to the one elicited by pleasant advertisements.
https://www.researchgate.net/publication/283459596_The_impact_of_product's_packaging_color_on_customers'_buying_preferences_under_time_pressure
Color perception is one of the most important cognitive features of human brain. Different colors lead to different cognitive activities and different mental arousal levels; as revealed by power spectral density obtained from EEG signals. As the color plays an important role in marketing and packaging industry; so neuro-marketing research; based on color stimuli is considered to be an important tool for market researcher. In order to assess the impact of each color; prime focus is to detect different colors from EEG signals. This study employs four color stimuli; e.g. red; green; yellow and blue; that were shown to various subjects and EEG signal corresponding to the mentioned stimulus was acquired. Power spectral density of each color was estimated and different activation areas of brain for each stimulus is illustrated in figures. This paper also employs an Interval-Type-II fuzzy space classifier for distinguishing between different stimuli that are considered for the concerned experiment. It is noted that classification rate is maximum for red color and minimum in case of yellow color. Precision and recall values also have been highlighted here. For detailed analysis; the performance of IT2FS classifier has been compared with other standard classifiers by Friedman Test.
The purpose of this study was twofold: (a) to investigate a neural mechanism of apparel product attractiveness and (b) to compare consumers’ brain responses to apparel product attractiveness with their self-reported responses. Based on Davidson’s frontal asymmetry theory, the researchers explored whether hemispheric asymmetry actually exists when consumers view apparel products with different levels of attractiveness. A total of 34 right-handed college students participated in the electroencephalography experiment. Measurements were obtained by recording the electrical activity of the left and right frontal areas of the brain while subjects were viewing apparel products. Supporting Davidson’s theory, the researchers found that a statistically significant difference of frontal asymmetry exists between attractive and unattractive apparel products. The findings of this study suggest that the frontal asymmetry score can be an alternative way to measure consumers’ unconscious responses to apparel product attractiveness.
Reducing the target customers' sensitivity to price is one way in which a firm can increase customer equity. To this end, actions related to corporate social responsibility (CSR) can invoke a sense of empathy among customers who respond to pro-social products with an increased willingness to pay (WTP). The purpose of this paper is therefore to determine the emotional mechanism of empathy and its neural correlates underlying consumers' positive reactions to pro-social marketing, which can increase WTP and customer equity. Study 1 is a behavioral study that intends to discover the underlying psychological mechanism of emotional empathy through which a message communicating CSR actions can increase customers' WTP for pro-social products. A neuromarketing study was conducted using electroencephalogram (EEG) to uncover the neural mechanism for customer empathy in response to CSR messages as enhanced theta-band activities of the anterior cingulate cortex (ACC), a frontal brain area that previous neuroscience research has linked to the affective empathic response.
In retail design, the use of color and light are important design techniques which aim to attract customers and increase market opportunities. The objectives of this study were to examine the effects of color and lighting on impression and identity of a bank sub-branch in Thailand. In this study, 144 research participants evaluated six computer-generated scenes with different color hues, lighting arrangements and correlated color temperatures using a semantic differential scaling method. The results show that different color and lighting conditions significantly affect both the impression of space and perception of retail identity. The results indicate that bank sub-branch space with chromatic conditions, especially space with warm color tone and colored-light, received a higher positive score of impression and identity. To fully understand the effect of color and lighting on space perception and retail identity, this study suggests that other color properties should be examined.
Emotions are time varying affective phenomena that are elicited as a result of stimuli. Videos and movies in particular are made to elicit emotions in their audiences. Detecting the viewers’ emotions instantaneously can be used to find the emotional traces of videos. In this paper, we present our approach in instantaneously detecting the emotions of video viewers’ emotions from electroencephalogram (EEG) signals and facial expressions. A set of emotion inducing videos were shown to participants while their facial expressions and physiological responses were recorded. The expressed valence (negative to positive emotions) in the videos of participants’ faces were annotated by five annotators. The stimuli videos were also continuously annotated on valence and arousal dimensions. Long-short-term-memory recurrent neural networks (LSTM-RNN) and Continuous Conditional Random Fields (CCRF) were utilized in detecting emotions automatically and continuously. We found the results from facial expressions to be superior to the results from EEG signals. We analyzed the effect of the contamination of facial muscle activities on EEG signals and found that most of the emotionally valuable content in EEG features are as a result of this contamination. However, our statistical analysis showed that EEG signals still carry complementary information in presence of facial expressions.
Despite its importance surprisingly little is known about the influence of colour in advertising. This paper studies the effects of the three components of an ad's colour (hue, saturation and lightness) on the emotions it evokes and on attitudes towards it. It is assumed that the influence of colour varies with the individual, whose optimal stimulation level (OSL) is considered a moderator variable. Analyses of covariance were conducted. The results show that OSL is a moderator variable accounting for the relations between hue and the pleasure evoked by the ad, and hue and the attitude towards the ad. Moreover, OSL proves to be a moderator for the relation between saturation and the same dependent variables.
The authors investigate the effect of red backgrounds on willingness-to-pay in auctions and negotiations. Data from eBay auctions and the lab show that a red (vs. blue) background elicits higher bid jumps. By contrast, red (vs. blue) backgrounds decrease price offers in negotiations. An investigation of the underlying process reveals that red color induces aggression through arousal. In addition, the selling mechanism—auction or negotiation—alters the effect of color by focusing individuals on primarily competing against other bidders (in auctions) or against the seller (in negotiations). Specifically, aggression is higher with red (vs. blue or gray) color and, therefore, increases bid jumps in auctions but decreases offers in negotiations.