Skip to main content
Download PDF

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

Brain Informatics volume 9, Article number: 27 (2022) Cite this article

Abstract

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.

1 Introduction

The present systematic review aimed to investigate the use of EEG measures in neuromarketing. We specifically focused on identifying which ERP and TF effects were most consistently associated with consumer preference and purchase intention, the best computational approaches to predict consumer behaviour, and which biometric measures are best combined with EEG measures to improve predictive accuracy.

Marketing is used to help a product inform, engage, and sustain its target audience by identifying and manipulating consumer preferences [167, 188, 288]. Conventional market research depends on self-report measures such as questionnaires, interviews, and focus-group discussions [167, 188, 288]. However, the traditional approach to marketing is usually only capable of conducting posteriori analysis of consumer preference towards marketing stimuli [288]. Self-report methods may also provide potentially unreliable or incomplete data due to participants misremembering their experiences or conforming to social desirability bias [288].

Neuromarketing overcomes the limitations of traditional marketing methods by capturing consumers’ unspoken cognitive and emotional responses to marketing stimuli using neuroimaging and biometric devices [11, 21, 30, 213, 304]. This allows for the concurrent recording of consumers’ emotional responses while engaging with marketing stimuli, and can detect emotional responses that consumers may be unwilling to report or may even be unware of [11, 21, 30, 213, 304]. The main techniques applied within this paradigm are neuroimaging measures, such as functional magnetic resonance imaging (fMRI) and electroencephalography (EEG); and biometric measures such as the galvanic skin response (GSR) and eye-tracking (ET) [163].

EEG measures and physiological measures (e.g., ET, GSR) are most commonly used in neuromarketing research due to them being relatively inexpensive and easy to implement [8, 130, 257]. EEG measurements are considered useful for their high temporal resolution and ability to adapt traditional experimental designs into neuroscience experiments [8, 130, 257], and are highly compatible with machine-learning algorithms due to the richness of data collected [59, 144, 164]. By comparison, physiological measures collect simpler data types, taken from bodily responses indexing changes in arousal, emotion and visual attention [18, 214, 221, 257, 290]. Heart-rate responses, GSR, and are typically used in neuromarketing stimuli to measure changes in physiological arousal in response to marketing stimuli, but cannot separate positive from negative responses [18, 214, 257]. Similarly, ET can be used to measure how marketing stimuli draw visual attention [10, 168, 221, 290] and facial expression analysis can be used to detect specific emotional responses to stimuli such as disgust or anger [60, 155, 187].

In contrast, neuroimaging measures such as fMRI are favoured due to their ability to accurately localise neural activity through detection of haemodynamic blood flow, but has poor temporal resolution when compared to EEG [134, 149, 224]. fMRI may also be less favourable in neuromarketing research than physiological and EEG measures due to the high cost associated with fMRI measures, and their lack of portability [134, 149, 224].

However, despite the current popularity of neuromarketing, it remains unclear which measures are the most effective and in which context they are best used. For example, while fMRI is often preferred for its spatial resolution and EEG is preferred for its temporal resolution [200], the comparative effectiveness of these measures in neuromarketing research is yet to be systematically investigated. Further, the data recorded by neuromarketing devices can be analysed in several ways and situations. For example, EEG data can be considered in terms of event-related potentials (ERPs) occurring milliseconds after the presentation of a stimulus [172, 184, 238], or in terms of changes in relative power in specified frequency bands [197, 256]. Recent developments in computer science have further allowed for machine-learning algorithms [2] to precisely predict consumer preference, purchasing behaviour, and remembered events [44, 51].

We, therefore, conducted a large-scale systematic review on the field of neuromarketing, with the express purpose of investigating the use of differing neuroimaging and biometric measures to determine their best use in the context of marketing research. To ease analysis, the systematic review was divided into three subsets based on broad neuromarketing measures: EEG measures, functional imaging measures, and biometric measures. The subset discussed presently focused on the use of EEG measures in neuromarketing. Specifically, the effectiveness of phase-locked and time–frequency (TF) analysis methods were compared, as well as sub-analysis methods within these fields (e.g., alpha- compared to theta-band activity). EEG combined with other neuromarketing measures (e.g., EEG combined with eye-tracking) was further investigated. Finally, the effectiveness of algorithmic approaches (e.g., machine-learning) to analysing EEG data were compared to traditional statistical methods.

The present article, therefore, focused its investigations on the following research questions:

  • What different analysis methods are currently used for EEG research in neuromarketing?

  • In which conditions are differing ERP components and TF effects significantly modulated?

  • Which EEG effects are best used to predict consumer preference and emotional responses to marketing stimuli?

  • In which ways are EEG measures best combined with other measures, and which measures are best combined?

  • Does machine learning improve the predictive accuracy of EEG measures in neuromarketing?

The research questions were generated in descending order to first identify the TF and ERP measures currently used in neuromarketing research, and then to investigate which emotional processes each effect was most consistently associated with. Following the initial identification of relevant EEG measures, consumer preference was investigated, and the effects most consistently associated with preference and purchase intention were assessed. Finally, the use of other biometric measures (e.g., ET, GSR) when combined with EEG was investigated, as well as the best computational approaches to predict consumer preference and purchase intention (e.g., machine learning algorithms compared to regression analyses).

These questions will allow us to gain a comprehensive account of the use of EEG measures in neuromarketing, which components are the most useful, and in which situations they are best used. After the introduction, the systematic review methodology will be discussed, followed by the systematic review findings, and a broader discussion of these findings.

2 Methods

A systematic literature review was performed to collect and assess the measures used in neuromarketing. This method was selected due to systematic literature reviews' high degree of objectivity and replicability [104]. A systematic review uses pre-defined methods to collect, select, and analyse collected literature, with the purpose of unbiased evidence collection to reach an impartial conclusion [135].

The present systematic review aimed to examine measures used in neuromarketing (i.e., neuroimaging and physiological measures) and identify the best use of these measures. To ease analysis, literature was subdivided into three categories; EEG measures, physiological measures, and functional imaging measures. The present paper represents the first subset of the systematic review; EEG measures. The analysis of the literature collected in this subset focused on comparing different EEG analysis approaches (e.g., TF and ERP analysis) in terms of their effectiveness when used in different kinds of neuromarketing research.

2.1 Search terms

Studies were collected from relevant journals and databases using key search terms. The primary search terms were the simple terms' neuromarketing' and 'consumer neuroscience', intended to catch studies that self-identified as belonging to the field of neuromarketing. However, additional studies may have used neuroscience or physiological measures to investigate marketing-relevant behaviour without explicitly using these terms. For this reason, additional search terms were used to identify studies that used neuroscience or physiological measures (e.g., EEG, MRI, GSR, ET) combined with marketing or consumer investigation.

The key search terms were used as selection criteria for the titles, keywords, abstracts, and body of text in the selected databases and journals. Document types included in the search were ‘articles’, and time limits were not established. Therefore, the initial search resulted in a shortlist of relevant publications to be considered for inclusion in the review. Duplicate articles were excluded from subsequent analysis.

To ensure the maximum number of neuromarketing studies were collected, literature searches were conducted in multiple databases and individual journals. Databases were selected from those that are internationally recognised and widely used as a source of research for distinct post-graduate programmes. Table 1 presents the database sources and the search terms used, and the number of articles found within each database.

Table 1 A summary of the journal databases searched, the search terms used for each database, and the number of journals produced from each search
Full size table

Twenty journals were searched to find any articles that were not found in the database search. These journals were selected due to their focus on consumer behaviour, marketing psychology, and neuroscience. Individual journals were searched using the keyword 'neuromarketing'. Table 2 presents the journals searched and the number of articles generated for each.

Table 2 A table summarising the journals search for the present systematic review and the number of articles produced by each search
Full size table

2.2 Inclusion/exclusion criteria

The inclusion criteria defined for the systematic review were as follows:

  • Primary study, published in a peer-reviewed journal.

  • Studies that explored marketing using brain or physiological mechanisms, underlying theories of marketing, consumer behaviour, psychology and neurology.

  • Research papers that used neuroimaging techniques such as EEG, fMRI, and positron emission tomography (PET) or physiological measures such as ET and GSR, to further understanding and application of marketing methods.

  • Research papers using exclusively human, non-clinical populations as participants.

Studies that explored new developments in neuromarketing or summarised current research were classed as review papers and not included in the review. However, review papers were screened for citations, and any relevant citations which were not included in the original search were added to the systematic review.

The exclusion criteria for the systematic review were defined as:

  • Any other literature review on Neuromarketing was excluded from the review.

  • Articles that were not published in peer-reviewed academic journal articles were excluded (e.g., book chapters, post-graduate theses, conference abstracts).

  • Articles written/published in any language other than English were excluded from the review.

2.3 Screening process

Overall, 2247 articles were found in the literature review. The titles and abstracts of these articles were individually analysed by multiple researchers, who screened the papers according to the pre-defined inclusion and exclusion criteria.

2.3.1 Process

  1. 1.

    Read all titles, exclude any duplicates and those that are clearly not relevant according to exclusion criteria.

  2. 2.

    Read abstracts of those remaining, exclude any that are not relevant according to exclusion criteria.

  3. 3.

    The remaining papers qualify for full-text screening.

  4. 4.

    Of those that qualify, search through their reference list and all published articles which cite them.

  5. 5.

    Compile this list and perform title and abstract screening (stages 1–3) again.

  6. 6.

    Repeat as many times as necessary until the compiled list from stage 5 yields no papers that qualify for full-text screening.

Following the first stage of article accumulation, articles were exhaustively screened by the authors through title and abstract reviews and were categorised according to whether they matched the inclusion or exclusion criteria. 2512 papers were excluded from the literature review, while 777 were included. Of the journals excluded from the review, 516 were duplicate articles, 268 were neuromarketing review papers, and 930 were removed according to the pre-defined exclusion criteria. 21 additional papers were extracted from the review papers, and these were added to the included from the initial literature search, meaning 798 papers were included in the literature review. A further 8 papers were included in the literature review due to a second search being conducted at a later date using the same search terms.

To aid the analysis of the collected data, papers were separated into four discrete categories based on the measures used. The first category included papers that used EEG measures to investigate consumer or marketing-related behaviour, in which 213 papers were identified. The second category included papers that used neuroimaging measures based on blood oxidation levels such as fMRI or FINRS, in which 158 papers were found. The third category described studies that used physiological measures such as ET or GSR, in which 410 studies were found.

All papers that used EEG or MEG measures were included in the current subsection of the systematic review. However, some of the papers included in the EEG subsection also used physiological methods such as ET or the GSR, and facial action coding and were therefore included in both the EEG and physiological subsections of the systematic review. EEG papers were categorised according to whether they primarily used ERP or TF analysis methods. Table 3 summarises the papers included in the EEG subset of the systematic review, along with the relevant measures used (ERP, TF, Mixed-Methods, Machine-learning).

Table 3 A summary of papers included in the current subset of the systematic review, categorised according to the measures used
Full size table

2.3.2 Secondary literature search

In order to identify neuromarketing literature which used machine-learning algorithms to predict consumer behaviour based on EEG signals, a secondary literature search was conducted. 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.

2.4 Data synthesis

In the data synthesis step, an aggregative approach was used to summarise the conclusions of the literature. Such an approach depends on the subjective interpretation of the researchers concerning the reviewed articles and, considering this, a certain degree of subjective latitude should be given to enable researchers to evaluate and compare distinct studies, with the purpose of extracting shared meanings and abstracting the approaches that do not concern the purposes stated for the review [73].

The overall objective of the present subset is to provide a mapping of the consistency and direction of significant EEG effects found in neuromarketing research, identifying specific components showing strong effects or consistencies. The results were be analysed using pattern correspondence [64].

3 Systematic review

3.1 EEG introduction

Cortical oscillatory activity can be analysed using three critical forms of information extracted from the waveform; amplitude, phase, and frequency [143]. Amplitude reflects the size of a peak in terms of its volts, while frequency measures how many oscillations occur per second (Hz), and phase represents the relative position of the wave in time. Using these forms of information, EEG data are typically analysed using one of two approaches; time–frequency analysis (TF) or event-related potential analysis (ERP).

3.2 Time–frequency analysis

EEG TF measures can expand on traditional marketing measures such as self-reports and behavioural willingness to pay by showing the underlying cognitive processes behind participant decision-making or the effect of specific changes to features of products or advertisements. EEG TF measures can further expand on physiological measures of arousal such as GSR by unveiling the underlying cognitive and emotional processes behind product and advertisement evaluation.

During time–frequency analysis, changes in the power of cortical oscillations are analysed according to pre-defined frequency bands, usually time-locked and averaged to a particular class of events [183]. Lower frequency bands generally exhibit larger amplitudes than higher frequency bands and usually reflect more extensive patterns of cortical activation [197]. Power changes can be analysed primarily according to a baseline condition (as is done in relative-band power or event-related desynchronisation analysis) across the scalp or relative to another region of the scalp (as is done in studies using measures of frontal asymmetry).

3.2.1 Frontal asymmetry

Consumer neuroscience is often focused on separating positive and negative responses to sales and marketing stimuli [63] to modify or predict consumer choices through behavioural, physiological, or neural measures [267, 269, 273, 274]. The relative difference in power between the left and right prefrontal cortex, especially in the alpha frequency band, has emerged as a critical measure separating positive from negative responses [252]. This neural marker is generally interpreted as reflecting the motivational direction and preference towards a stimulus and often occurs just before the formation of behavioural intentions [108, 112, 275]. Frontal asymmetry is posited to reflect an approach response to stimuli when indicating an increase in cortical activity to the left side and an avoidance response when indicating an increase in cortical activity to the right side [108, 112]. For this reason, frontal asymmetry is thought to be a more nuanced measure of preference than physiological measures such as GSR, which can only identify valence magnitude, not direction [108, 112].

The relative degree of alpha frontal asymmetry is calculated using the following formula:

$$\frac{Left \,alpha \,power - right \,alpha \,power}{left \,alpha \,power + right\, alpha\, power}*100.$$

Frontal asymmetry was commonly found during positive elements of viewed advertisements [15, 52, 108, 112, 157, 182, 203, 204, 227, 277], as well as proving useful in the prediction of advertisement preference and success [46, 51, 54, 157, 182, 203, 204, 266, 266, 270, 270, 271, 271, 272, 272, 275, 277]. Frontal asymmetry has also been shown to differentiate between emotional responses to advertisements by gender [267, 269, 273, 274] and age [267, 269, 273, 274] and shows approach/avoidance responses to marketing-related stimuli such as food [39, 207, 232, 278], music [16] and sales/persuasion messaging [58, 65].

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.

While frontal asymmetry is most commonly associated with alpha-band activity [108, 112, 275], effects were also reported in the theta and beta bands in the reviewed literature. Frontal asymmetry is most widely reported in the alpha band and appears to reflect WTP and advertisement effectiveness, therefore likely reflecting the motivational approach response most commonly associated with frontal asymmetry [44,45,46, 50, 65, 148, 157, 182, 191, 192, 203, 204, 218, 219, 227, 237, 239, 267, 267, 269, 269, 273, 273, 274, 274, 275, 277, 278]. Frontal asymmetry reported in the theta-band mostly related to self-reported preference [16, 138, 266, 270,271,272, 275], and advertisement memorability [44, 46, 182, 266, 266, 270, 270, 271, 271, 272, 272]. Further, while two experiments reported significant changes in beta-band frontal asymmetry [9, 182, 217], the cognitive processes reflected by this response seem much less certain. Therefore, it is recommended that frontal asymmetry measures are primarily employed in the alpha band and are best used in neuromarketing when assessing approach/avoidance responses to advertisements and willingness to pay towards products.

3.2.2 Relative-band power changes

While frontal asymmetry is considered to be the best measure of motivational valence, relative-band power changes in specific frequency bands can be used to measure other cognitive and emotional responses to marketing stimuli. For example, a reduction in power in the alpha band (8–12 Hz) over occipital areas is generally considered to reflect visual processing. Occipital alpha-band power was found to be modulated by video but not print advertisements in early experiments [62], and has been used in more recent experiments to measure the visual processing of advertisement features [83, 147, 228, 229, 253, 300].

Frontal alpha-band suppressions are linked to information processing, attentional orienting, decision-making and emotional regulation [55, 143, 190, 309], and were shown in the reviewed literature to relate to advertisement recall and effectiveness [68, 71, 74, 90, 109, 145, 267, 269, 273, 274] (Pozharliev 2022), TV viewership [237] and self-reported preference [26, 82, 83, 118, 119] as well as responding to preferred brands and pro-social products [146, 161, 244]. Power changes in the alpha band over frontal regions of the scalp can therefore be considered to be a helpful neuromarketing tool, especially when considering the saliency of advertisement and product features.

Theta rhythms describe slower and larger oscillatory frequencies within the 4–7 Hz range [235]. An increase in midline theta power is reliably associated with long-term memory encoding [95, 142, 234] and an increase in sustained effortful engagement [47, 121, 169]. The papers reviewed showed that theta-power was commonly associated with memorable elements of advertisements [68, 70, 74, 132, 145, 266, 266, 267, 267, 269, 269, 270, 270, 271, 271, 272, 272, 273, 273, 274, 274], recognised brands [68, 70, 74, 132, 145, 146, 171, 191, 192, 195, 245, 266, 266, 267, 267, 269, 269, 270, 270, 271, 271, 272, 272, 273, 273, 274, 274] and out-of- and within-sample success [44, 46, 68, 70, 74, 83, 90, 109, 117, 132, 145, 146, 158, 171, 191, 192, 195, 237, 245, 258, 266, 266, 267, 267, 269, 269, 270, 270, 271, 271, 272, 272, 273, 273, 274, 274]. However, preference associations were less common [19, 20, 66, 118, 119, 160,161,162, 216, 244, 295]. Increases in theta-band power can be considered a valuable tool in advertising research, highlighting the memorability and out-of-sample effectiveness of tested advertisements, but may be less useful in investigating buying behaviour or preference.

Faster cortical oscillatory activity found in the beta (16–24 Hz) and gamma (30–45 Hz) ranges are less clearly interpreted in neuromarketing research. Beta-band power is traditionally associated with movement preparation and intention formation when suppressed over sensorimotor regions [72, 208, 209], as well as inhibition when increased over right frontal areas [40, 276], while gamma-band over prefrontal areas is associated with visual attention [233], working memory [93, 230], and language abilities [93]. However, in the literature, beta and gamma band changes were modulated by a range of stimuli, including advertisement memorability [13, 14, 17, 74, 194], preference [53, 89, 115, 158, 171, 296], emotional valence [89, 296, 298], and changes in the shopping environment [27, 28, 116]. Therefore, relative-band power changes in the beta and gamma bands should be treated with caution when used in neuromarketing research. Further investigation is required to identify their exact role in buying and advertising behaviour.

Other EEG TF measures have been used in neuromarketing research, such as cross-brain correlations across two participants predicting advertisement preference and recall [24], or global field power and peak density function [13, 14, 94, 105, 106, 266, 266, 266, 267, 269, 270, 270, 270, 271, 271, 271, 272, 272, 272,273,274], and partial directed coherence [74]. Additionally, pre-defined emotion toolboxes have been used to gauge emotional responses to marketing stimuli based on EEG TF responses [21, 63, 115, 199, 231]. However, due to the limited research done, it is difficult to judge the consistency of these measures, so further research is required.

3.2.3 Mixed measures experiments

A subsection of the studies reviewed used a mix of EEG time–frequency measures and physiological measures; including measures of arousal, such as the GSR heart-rate variability (HRV) and pupil dilation (PD); ET measures of attentional orienting; and facial-expression analyses. The use of a mixed-measures design allows for the comparison of EEG TF measures with physiological measures, identifying the strengths and weaknesses of each and determining which measures should be used in which contexts [56].

Several studies have shown that measures of arousal and EEG TF responses are modulated by advertisement preference and memorability [1, 44, 46, 58, 83, 203, 266, 266, 267, 267, 269, 269, 270, 270, 271, 271, 272, 272, 273, 273, 274, 274], as well as differences in product features [43, 50, 278]. However, physiological measures of arousal appear to be unable to differentiate between different emotional and cognitive responses, while EEG TF measures can [1, 22, 24, 50, 52, 83, 110, 189, 191, 192, 195, 232, 266, 273]. For example, [50] Chen [49] showed that HRV measures of arousal could only differentiate between the intensity of mouthwash flavours, while FAA distinguished between flavours and was predictive of self-reported preference and purchase intention. It, therefore, appears that physiological measures of arousal are less useful when combined with EEG TF measures due to their lack of sensitivity.

ET, or the tracking of eye movements, is a measure that can easily be combined with EEG measures while participants view an advertisement or product [53, 54, 68, 82, 83, 99, 191, 192, 195, 267, 269, 273, 274]. The advantage of this measure over EEG TF measures is that it can be used to identify product or advertisement features that draw attention within a visual field [15, 32, 53, 68, 82, 83, 99, 189, 267, 269, 273, 274, 287] (Pozharliev et al. 2022). Further, Zhu et al. [314] found that, while EEG can be used to build more accurate machine learning models of customer preference than ET, the inclusion of ET data does improve the predictive accuracy of the model when compared to models using EEG data alone. ET, therefore, provides a useful and complementary measure to EEG TF measures. Some papers found significant attentional ET effects, but no significant EEG differences [32, 82]. ET should therefore be considered for use with EEG measures.

Only eight studies identified have combined facial expression analysis or EMG with EEG TF analyses [15, 35, 36, 54, 115, 203, 278] (Berčík et al. 2021). However, this measure provided a useful complement to EEG TF measures, as facial expressions and micro-expressions can separate different emotional responses such as happiness and disgust. Although the combination of EEG TF and facial-expression analysis is currently not well employed in neuromarketing research, further exploration should be pursued.

3.2.4 Machine-learning prediction

In more recent years, neuromarketing researchers have begun to use machine-learning classification of EEG TF measures to improve the prediction of ‘like/dislike’ or pleasantness ratings. 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 [2,3,4, 6, 23, 87, 98, 102, 103, 206, 210, 251, 254, 263, 287, 294, 307, 308] (see Table 4).

Table 4 A systematic table summarising the machine-learning studies included in the present review, including the features, labels, and algorithms used, as well as the accuracy rates achieved
Full size table

Across the reviewed literature, the most commonly used classification methods were DNN [2, 3, 6, 313], KNN [2, 3, 6, 98, 102, 103, 206, 210, 263, 308], SVM [1,2,3, 6, 87, 98, 102, 103, 185, 185, 186, 186, 210, 263, 291, 307, 308, 314], RF [1,2,3, 6, 23, 85, 98, 151, 206, 307, 308, 314], and regressions [1, 24, 85, 102, 103, 138, 237, 251].

The highest predictive accuracy reported was found by experiments using DNN algorithms, which achieved a binary classification accuracy of 85–94% (M = 0.89, SD = 0.06), followed by RF algorithms (M = 0.80, SD = 0.11), then SVM algorithms (M = 0.77, SD = 0.11), and KNN algorithms (M = 0.72, SD = 0.13). The worst classification accuracy was reported by studies using regression methods, which were only able to correctly classify consumer preference around 60% of the time (M = 0.59, SD = 0.24).

Machine-learning prediction appears to be a fruitful avenue of research within neuromarketing and can achieve very high predictive accuracy, potentially overcoming the reliability problems identified in earlier portions of the systematic review. However, there are several additional methodological considerations that researchers must take into account when using machine-learning prediction. Crucially, machine-learning algorithms may require a larger number of trials and greater computational power than traditional regression models, due to the complex calculations required [136]. Machine learning models are also vulnerable to ‘overfitting’, where models achieve high accuracy rates for training data, but perform poorly when predicting out-of-sample values [67, 225, 302]. Current neuromarketing research using machine-learning prediction also seems relatively limited, primarily focusing on product preference, so this method should be expanded in the future.

3.3 Event-related potential measures

ERPs reflect averaged transient effects to specific stimuli or a specific class of stimuli [172, 183, 238]. ERPs, therefore, measure phasic responses to advertising stimuli, occurring within hundreds of milliseconds following stimulus onset [172, 238], and thus capitalise on the high temporal resolution of EEG recordings. It is generally considered that reactions that occur within the first 300 ms of decision-making are unconscious, while those occurring after 300 ms are related to conscious inclinations [165, 193]. The consensus regarding whether the P300 and N400 components reflect conscious or unconscious reactions remains contentious, and these components may reflect a critical phase in the transition from unconscious to conscious mental processes [57, 156, 250].

3.3.1 N400/N200 ERP component

The N200 and N400 components are most commonly associated with conflict and unfamiliarity, especially brand extension and recognition [48, 152, 240, 292]. The N200 is a negative potential peaking between 200 and 350 ms after stimulus onset, with an amplitude that is negatively related to familiarity and is generally considered to represent fast and unconscious conflict processing [48, 152, 240, 292]. The N400, a negative potential peaking around 400 ms following stimulus onset, is commonly related to violations of grammatical rules and unexpected stimuli. N400 amplitude is thought to reflect the corresponding conscious processing of conflicting information [48, 152, 240, 292]. These components are most commonly used in neuromarketing research to measure consumer familiarity with brands and products and the conflict between price and expected value. In the reviewed literature, fourteen studies using brand-extension paradigms reported modulations in N200/N400 amplitude [42, 76, 80, 125, 175, 177, 179, 180, 246, 248, 286, 295, 299, 312], nine studies reporting N200/N400 modulations used conflict tasks such as oddball tasks [79, 81, 97, 100, 107, 126, 129, 205, 259, 311], and nine used other tasks, such as auction tasks or advertising stimuli [91, 92, 105, 106, 124, 128, 137, 279, 279, 282, 282, 284, 284, 285, 285, 310].

In the literature, significant N200 and N400 amplitude differences were most commonly found in experiments utilising brand extension paradigms. Brand extension tasks are used to investigate how generic product types (e.g., coffee) are associated with brand names or logos (e.g., Starbucks). Twelve out of the 15 studies which investigated brand extension in the reviewed literature found significant effects in N200 or N400 amplitude [76, 80, 125, 177, 179, 180, 246, 248, 286, 297, 299, 312], while one used machine-learning and therefore did not investigate modulations in ERP amplitude directly [178]. Within the 11 studies that reported significant N200/N400 effects, nine reported significant effects of N400 amplitude [42, 80, 125, 177, 180, 246, 286, 295, 299], two reported significant N200 effects [76, 179], while only two studies found significant effects in both N400 and N200 amplitude [80, 299]. Further, two studies reported non-significant N2 amplitude modulations [297, 299]. In contrast, six of the studies using brand extension experiments reported significant differences in P300 amplitude, and only two found significant LPP or LPC differences [76, 246]. N200 and N400 amplitudes were generally interpreted as reflecting conflict processing and were negatively related to brand extension acceptance rates [42, 76, 80, 177, 179, 180, 246, 286, 295].

N400 and N200 amplitudes have also been associated with perceptions of brands and products, especially when conflicted with other relevant features such as reviews or previous experiences. It was found by nine of the reviewed studies that N200/N400 amplitudes were significantly modulated by participant awareness of a brand/product [105,106,107, 175, 279, 282, 284, 285, 310, 311]. When elicited during oddness experiments, N200/N400 amplitudes were most commonly modulated by incongruence caused by negative framings or reviews during the viewing of a product or brand [49, 81, 97, 100, 126, 166, 205], as well as product preference [91, 92, 124, 137, 175, 279, 281, 282, 284, 285]. Three studies investigated the effect of price on N400 and N200 amplitudes [79, 129, 137] and found that amplitudes were modulated by violations in price expectations and price deception.

Overall, the N200 and N400 ERP components are typically used in neuromarketing research to identify brand familiarity, extension, and conflict caused by negative attitudes and price violations. Reported effects of preference on N400/N200 amplitudes were less consistent and should be treated with caution. Significant effects were more commonly found in N400 than N200 amplitudes, suggesting that response conflict occurs more consciously in consumers.

3.3.2 P300/P200 ERP component

The P2 and P300 ERP components are positive potentials occurring between 200 and 400 ms after cue onset. It is generally considered that P300 amplitude is positively related to the attentional resources allocated towards a stimulus [133, 140, 196]. The P2 is a similar positive potential, which peaks approximately 200 ms after stimulus presentation [114, 280, 293], and is considered to reflect the rapid automatic activity of attention [113, 114, 280, 293]. Based on the theoretical background of P300 and P200 modulations, it would be expected that their best use in neuromarketing would emerge in investigating how products and advertisements attract attention, and the best ways to draw attention.

In the present review, P300 and P2 amplitude were found to be especially effective in the investigation of advertisement [61, 62, 261, 279, 282, 284, 285, 289] and marketing [212, 215, 259, 264, 315] effectiveness, with significant amplitude modulations found in all reviewed studies. P300 and P200 amplitude were also significantly modulated by preference [25, 126, 137, 174, 174, 176, 176, 181, 181, 264, 281, 306], purchase intention [41, 92, 127, 129, 160, 162, 170, 174, 176, 181, 247], price [128, 223], and brand or product features [75, 81, 105,106,107, 311]. However, significant effects were not reported in all papers investigating these factors [37, 49, 79, 91, 92, 111, 131, 166, 205, 249, 258], so results may lack statistical power. Especially of note were the inconsistent effects found regarding preference on P200 amplitude, with some papers finding smaller amplitudes to preferred stimuli [107, 124, 126, 129, 170, 315], and others finding the reverse [25, 128, 174, 174, 176, 176, 181, 181, 306].

Overall, P300 and P2 amplitudes were revealed to be especially effective when investigating advertising effectiveness. However, modulations in these ERP components should be treated with caution when examining consumer preference as it may lack statistical power and may only be reflective of attention drawn due to stimulus salience rather than valence.

3.3.3 LPP component

The LPP is a positive component, usually found later than 400 ms following stimulus onset, and is generally considered to reflect conscious emotional processing. The LPP is sensitive to emotional stimuli, both positively and negatively valanced [78, 153, 241, 242], and has been proposed to represent emotional regulation processing or attention towards the emotional nature of stimuli. The LPP is commonly used in neuromarketing due to its relationship to conscious emotional evaluation, which is strongly related to purchase behaviour and brand perception [25, 37, 166, 174, 176, 181].

In the reviewed literature, the LPP was most commonly associated with preference or emotional evaluation towards products and brands [25, 37, 75, 79, 91, 92, 126, 128, 166, 174, 174, 174, 176, 176, 176, 181, 181, 181, 198, 215, 247, 249, 306, 310, 315], while only six papers investigating participant preference did not report significant modulations in LPP amplitude [62, 137, 154, 258, 261, 281]. However, the effect of emotional content on LPP amplitude appears to predominantly be reflective of valence strength rather than direction [128, 249, 315], meaning it may be unable to differentiate between positive and negative attitudes towards brands and products. The results reported by Goto et al. [92] further showed that when used to predict single-trial product preference, the LPP achieved the highest accuracy (70%) of all the ERP components investigated. However, it may be less sensitive under low trial numbers [107].

Taken together, the reviewed literature reveals the LPP as a key ERP component in neuromarketing research, as it directly reflects emotional evaluation of brands and products, rather than the correlated measures of attention and conflict. For this reason, the LPP may be a more appropriate measure in neuromarketing research for the investigation of preference. However, the LPP should be used with caution, as it may be unable to untangle emotional valence and may only be reflective of intensity.

3.3.4 Other ERP components

A limited number of studies identified in the literature review investigated further ERP components in relation to marketing stimuli, including the MMN [111], N1 [25, 97, 100, 205, 282, 289, 306], FRN [41, 236], LPC [129, 246, 279, 282, 284, 285], and PSW [92]. However, due to the limited number of studies investigating the effectiveness of these ERP components, it is difficult to make explicit judgments regarding their use and effectiveness.

3.3.5 ERP studies using machine learning/ICA

Three studies were identified that used independent component analysis or machine-learning on phase-locked EEG data rather than traditional ERP analysis. Tyson-Carr et al. [262] used an independent component analysis (ICA) to investigate ERP effects behind willingness-to-pay, finding that significant differences between EEG activity between the right and left parietal lobe at around 200 ms following stimulus onset were most predictive of willingness-to-pay. Similarly, Roberts et al. [223] differentiated between the phase-locked EEG activity found in response to high- and low-value items using ICA analysis. Ma and Zhuang [173] was the only study identified in the present systematic review which used machine-learning to investigate marketing-relevant stimuli based on phase-locked EEG activity. Using t-SNE machine learning, the researchers predicted brand-extension acceptance with an accuracy of 87.37%.

3.3.6 ERP conclusions

Taken together, the literature collected in the present review identified three ERP components that were most commonly used in neuromarketing research: N400, P300, and LPP. Modulations in N400 and P300 amplitude were best implemented when investigating specific neuromarketing effects such as conflict and attentional saliency. In contrast, LPP amplitude modulations appeared more suitable to measure preference and emotional evaluation, although it is only sensitive to magnitude, not valence. The use of alternate data analytic approaches such as machine learning and ICA is less common in ERP analysis than TF analysis. However, studies in this area seem promising. It is recommended that future ERP research in neuromarketing employs machine-learning and ICA analyses.

4 Discussion

The results of the present systematic review revealed several key recommendations that can be made regarding the use of EEG measures in neuromarketing. First, key ERP and TF components were identified as the most consistent markers of preference and emotional evaluation, namely the FAA and LPP. Second, the importance of machine-learning analysis in future neuromarketing research was highlighted. Finally, it was shown that EEG measures are best used in conjunction with ET and facial expression analysis rather than GSR or PD.

The core finding of the present systematic review was the identification of FAA and LPP as key TF and ERP components in the investigation of consumer preference. Overall, FAA was judged to be the optimal measure of preference due to its ability to disentangle positive from negative responses, while the LPP only indexed response magnitude. Further components were identified that were useful in indexing customer attention (P300 amplitude, alpha-band power, theta-band power), memorability (theta-band power) and emotional conflict (N400 amplitude). These components should be considered in future neuromarketing research but not used as principal measures of consumer preference.

Traditional marketing models assume that consumer decisions are mostly rational, and therefore ignore the role of implicit emotional responses in consumer preference and buying decisions [7, 69]. Neuromarketing overcomes these limitations through the use of biometric and neuroimaging measures, which can detect implicit emotional responses traditionally ignored in marketing research [7, 69, 220]. The primary benefits of neuromarketing are therefore to improve the accuracy of models aiming to predict consumer preference and buying behaviour, and provide a greater understanding of the emotional impact of marketing stimuli on consumers [7, 69, 220]. Ultimately, neuromarketing research should be developed in ways that can be actively used to improve products or advertising campaigns. However, there was a large degree of inconsistency found in the reviewed literature regarding the significance and interpretation of different EEG effects in a marketing context, specifically when relating to consumer preference.

The present results, therefore contribute to the literature by demonstrating the most consistent EEG measures of consumer preference and willingness to pay, and these measures require greater focus in future research. Matching the theoretical literature, FAA appears to reflect approach/avoidance responses to stimuli [108, 275] and was the only component identified in the current review that could untangle positive from negative emotional reactions. In contrast, the LPP ERP component appears to reflect conscious emotional processing of marketing stimuli [78, 153, 241, 242] but cannot separate positive from negative responses. Future neuromarketing research should therefore focus on the use of the FAA and LPP when creating predictive models of consumer preference.

It would also be appropriate to use the other components identified as measures of factors that may indirectly relate to preference. For example, P300 amplitude and theta-band power appear to be effective measures when investigating advertisement effectiveness, reflecting attentional orienting [133, 140, 196] and memory encoding [95, 143], respectively. In contrast, the N400 ERP component was most effective in investigating brand extension acceptance rates. Future research investigating these components should therefore build their hypotheses based on these findings and not use them as undifferentiated measures of preference.

When used in a mixed-measures design, EEG data were found to be best combined with eye-tracking and facial-expression analysis, as these provide data types that EEG cannot reveal. ET measures are useful in demonstrating which areas of a visual field (e.g., advertisement) draw customer attention [32, 83]. Similarly, facial-expression analyses can reveal specific emotional responses to marketing stimuli such as joy or disgust [15, 54, 115, 203, 278]. In contrast, physiological measures of arousal such as GSR and PD provide less additional interpretive utility as they only reflect arousal intensity [1, 24, 50, 52, 83, 189, 191, 192, 195, 232], which can be indexed by EEG measures such as the FAA or P300.

It is our hope that the interpretive framework provided in the present review will aid in the analysis and interpretation of future neuromarketing research, and provide a neuromarketing-specific interpretation of EEG data, preventing post hoc analysis of future results. Highlighting the importance of a clear interpretive framework, significant inconsistencies were found across several sections of the systematic review, and future researchers should be aware of the issues of replication in neuromarketing research. For example, several studies found a positive relationship between preference and P2 amplitude [107, 124, 126, 129, 170, 315], while others found a negative relationship [25, 128, 174, 174, 176, 176, 181, 181, 306]. These inconsistencies reflect the larger replication crisis in psychology and may result from small effect sizes, cherry-picking of results, and the overuse of frequentist statistics.

Machine-learning algorithms are a potential solution to the replication problem, achieving high predictive accuracy in the reviewed literature, consistent across papers. The most effective machine-learning algorithms were DNNs, which reported accuracies as high as 94% in predicting consumer preference. The predictive accuracy of machine learning was further improved when conducted on EEG data combined with physiological measures. Therefore, the present results highlight the importance of machine-learning analyses in future neuromarketing research to improve the replicability and consistency of results.

While machine learning presents a promising avenue for neuromarketing research, care should be taken when designing machine learning models. First, due to the complex calculations made, machine learning requires more trials and computational power than traditional statistical models [136, 222]. Machine learning models are also vulnerable to ‘overfitting’, where they may show high accuracy rates for the training data used, but perform poorly when predicting out-of-sample values. Overfitting can be solved in a number of ways, such as by splitting a dataset into ‘training’ and ‘testing’ data, or by using out-of-sample data to test machine-learning models [67, 225, 302].

In the literature reviewed presently, machine learning was used primarily to predict consumer self-reported preference or buying behaviour [1,2,3,4, 6, 23, 24, 31, 85, 87, 96, 98, 101,102,103, 139, 151, 178, 185, 185, 186, 186, 206, 210, 223, 254, 262, 263, 287, 294, 307, 308, 314]. However, it has yet to be demonstrated how machine learning can be used to improve advertisement or product designs. For example, a machine learning approach could be used to suggest the shape or colour to use on product packaging. The integration of EEG machine learning methods with developing technologies such as VR headsets should also be investigated further (Fortunato 2014), as well as the use of ‘live’ machine-learning, which can actively update stimuli while a consumer is viewing them based on their patterns of brain activation (Robaina-Calderin 2021; Fortunato 2014).

5 Conclusion

The literature summarised in the present systematic review highlighted the effectiveness of FAA and the LPP as measures of consumer preference and pointed to the importance of machine learning to tackle problems of consistency and replicability existent in the current literature. It is recommended that in future research, investigators use LPP and FAA effects when investigating customer preference and only use other EEG components to investigate other specifically associated effects (e.g., memory encoding, attentional orienting). Further, the use of machine learning is encouraged in future research to improve the replicability of EEG measures of customer preference, and the scope of machine-learning should be expanded.

Availability of data and materials

The search terms and inclusion/exclusion criteria required for replicability of the present research are included in the manuscript text.

Abbreviations

DNN:

Deep Neural Network

EEG:

Electroencephalography

ERP:

Event-related potential

ET:

Eye-tracking

fMRI:

Functional magnetic resonance imaging

fNIRS:

Functional near-infrared spectroscopy

GSR:

Galvanic skin response

HRV:

Heart-rate variability

ICA:

Independent component analysis

FAA:

Frontal alpha asymmetry

FRN:

Feedback related negativity

LPC:

Late positive complex

LPP:

Late positive potential

MMN:

Mismatched negativity

PD:

Pupil dilation

PET:

Positron emission topography

PSW:

Positive slow wave

TF:

Time–frequency

References

  1. Adrián CG, Fuentes-Hurtado F, Valery NO, Provinciale JG, Ausín JM, Mariano AR (2016) A comparison of physiological signal analysis techniques and classifiers for automatic emotional evaluation of audiovisual contents. Front Comput Neurosci 10:74. https://doi.org/10.3389/fncom.2016.00074

    Article  Google Scholar 

  2. Aldayel M, Ykhlef M, Al-Nafjan A (2020) Deep learning for EEG-based preference classification in neuromarketing. Applied Sci 10(4):1525. https://doi.org/10.3390/app10041525

    Article  Google Scholar 

  3. Aldayel M, Ykhlef M, Al-Nafjan A (2021) Recognition of consumer preference by analysis and classification EEG signals. Front Hum Neurosci 14:604639. https://doi.org/10.3389/fnhum.2

    Article  Google Scholar 

  4. Alimardani M and Kaba M (2021) Deep learning for neuromarketing; classification of user preference using EEG signals. In: Paper presented at the 12th Augmented Human International Conference, p 1–7. https://doi.org/10.1145/3460881.3460930020.604639

  5. Alonso Dos Santos M, Calabuig Moreno F (2018) Assessing the effectiveness of sponsorship messaging: measuring the impact of congruence through electroencephalogram. Int J Sports Market Sponsors 19(1):25–40. https://doi.org/10.1108/IJSMS-09-2016-0067

    Article  Google Scholar 

  6. Al-Nafjan A (2022) Feature selection of EEG signals in neuromarketing. PeerJ Comp Sci 8:e944. https://doi.org/10.7717/peerj-cs.944

    Article  Google Scholar 

  7. Amran AS, Ibrahim SA, Malim NH, Hamzah N, Sumari P, Lutfi SL, Abdullah JM (2022) Data acquisition and data processing using electroencephalogram in neuromarketing: a review. J Sci Technol. https://doi.org/10.47836/pjst.30.1.02

    Article  Google Scholar 

  8. Andrejevic M (2012) Brain whisperers: cutting through the clutter with neuromarketing. Somatechnics 2(2):198–215. https://doi.org/10.3366/soma.2012.0057

    Article  Google Scholar 

  9. Aprilianty F, Purwanegara MS (2016) Effects of colour towards underwear choice based on electroencephalography (EEG). Australas Mark J 24(4):331–336. https://doi.org/10.1016/j.ausmj.2016.11.007

    Article  Google Scholar 

  10. Arch DC (1979) Pupil dilation measures in consumer research: applications and limitations. N Am Adv 1:166–168

    Google Scholar 

  11. Ariely D, Berns GS (2010) Neuromarketing: the hope and hype of neuroimaging in business. Nat Rev Neurosci 11(4):284–292. https://doi.org/10.1038/nrn2795

    Article  Google Scholar 

  12. Astolfi L, De Vico Fallani F, Cincotti F, Mattia D, Bianchi L, Marciani MG, Salinari S, Colosimo A, Tocci A, Soranzo R, Babiloni F (2008) Neural basis for brain responses to TV commercials: a high-resolution EEG study. IEEE Trans Neural Syst Rehabilit Eng 16(6):522–531. https://doi.org/10.1109/TNSRE.2008.2009784

    Article  Google Scholar 

  13. Astolfi L, Fallani FDV, Cincotti F, Mattia D, Bianchi L, Marciani MG, Salinari S, Gaudiano I, Scarano G, Soranzo R (2009) Brain activity during the memorization of visual scenes from TV commercials: an application of high resolution EEG and steady state somatosensory evoked potentials technologies. J Physiol 103(6):333–341. https://doi.org/10.1016/j.jphysparis.2009.07.002

    Article  Google Scholar 

  14. Astolfi L, Vecchiato G, De Vico Fallani F, Salinari S, Cincotti F, Aloise F, Mattia D, Marciani MG, Bianchi L, Soranzo R, Babiloni F (2009) The track of brain activity during the observation of TV commercials with the high-resolution EEG technology. Comp Intell Neurosci. https://doi.org/10.1155/2009/652078

    Article  Google Scholar 

  15. Ausin-Azofra JM, Bigne E, Ruiz C, Marín-Morales J, Guixeres J, Alcañiz M (2021) Do you see what i see? Effectiveness of 360-Degree vs. 2D video ads using a neuroscience approach. Front Psychol. https://doi.org/10.3389/fpsyg.2021.612717

    Article  Google Scholar 

  16. Avinash T, Dikshant L, Seema S (2018) Methods of neuromarketing and implication of the frontal theta asymmetry induced due to musical stimulus as choice modeling. Proced Comp Sci 132:55–67. https://doi.org/10.1016/j.procs.2018.05.059

    Article  Google Scholar 

  17. Babiloni F, Cincotti F, Mattia D, Mattiocco M, Bufalari S, Fallani FD, Tocci A, Bianchi L, Marciani MG, Meroni V, Astolfi L (2006) Neural basis for the brain responses to the marketing messages: an high resolution EEG study. In: Conference proceedings: annual International conference of the IEEE engineering in medicine and biology society. p 3676–9

  18. Bagozzi RP, Verbeke WJ (2014) Biomarketing an emerging paradigm linking neuroscience, endocrinology, and genetics to buyer-seller behavior. In: Moutinho L, Bigne E, Manrai AK (eds) The Routledge companion to the future of marketing. Routledge, Milton Park

    Google Scholar 

  19. Balconi M, Sebastiani R, Angioletti L (2019) A neuroscientific approach to explore consumers’ intentions towards sustainability within the luxury fashion industry. Sustainability 11(18):5105. https://doi.org/10.3390/su11185105

    Article  Google Scholar 

  20. Balconi M, Sebastiani R, Galeone AB, Angioletti L (2020) Sustainability in the fashion luxury branding. Using neuroscience to understand consumers’ intentions towards sustainable eco-luxury items. Neuropsychol Trends 27:65–74. https://doi.org/10.7358/neur-2020-027-ball

    Article  Google Scholar 

  21. Baldo D, Parikh H, Piu Y, Müller K (2015) Brain waves predict success of new fashion products: a practical application for the footwear retailing industry. J Creating Value 1(1):61–71. https://doi.org/10.1177/2394964315569625

    Article  Google Scholar 

  22. Baldo D, Viswanathan VS, Timpone RJ, Venkatraman V (2022) The heart, brain, and body of marketing: complementary roles of neurophysiological measures in tracking emotions, memory, and ad effectiveness. Psychol Market. https://doi.org/10.1002/mar.21697

    Article  Google Scholar 

  23. Bandara SK, Wijesinghe UC, Jayalath BP, Bandara SK, Haddela PS, Wickramasinghe LM (2021) EEG Based Neuromarketing Recommender System for Video Commercials. In: Paper presented at the 2021 IEEE 16th International Conference on Industrial and Information Systems (ICIIS), p 11–16. https://doi.org/10.1109/ICIIS53135.2021.9660742

  24. Barnett SB, Cerf M (2017) A ticket for your thoughts: method for predicting content recall and sales using neural similarity of moviegoers. J Cons Res 44(1):160–181. https://doi.org/10.1093/jcr/ucw083

    Article  Google Scholar 

  25. Bastiaansen M, Straatman S, Driessen E, Mitas O, Stekelenburg J, Wang L (2018) My destination in your brain: a novel neuromarketing approach for evaluating the effectiveness of destination marketing. J Destin Market Manage 7:76–88. https://doi.org/10.1016/j.jdmm.2016.09.003

    Article  Google Scholar 

  26. Bercik J, Horska H, Viragh R, Sulaj A (2017) Advanced mapping and evaluation of consumer perception and preferences on the car market based on eye-tracking. J Manag Stud 16(2):28–39. https://doi.org/10.17512/pjms.2017.16.2.03

    Article  Google Scholar 

  27. Berčík J, Horská E, Gálová J, Margianti ES (2016) Consumer neuroscience in practice: the impact of store atmosphere on consumer behavior. Soc Manag Sci 24(2):96–101. https://doi.org/10.1108/EJM-02-2017-0122

    Article  Google Scholar 

  28. Berčík J, Horská E, Wang RWY, Chen Y (2016) The impact of parameters of store illumination on food shopper response. Appetite 106:101–109. https://doi.org/10.1016/j.appet.2016.04.010

    Article  Google Scholar 

  29. Berezka SM, Sheresheva MY (2019) Neurophysiological methods to study consumer perceptions of television advertising content. Vestn St Peterbg 18(2):175–203. https://doi.org/10.21638/11701/spbu08.2019.202

    Article  Google Scholar 

  30. Berns GS, Moore SE (2012) A neural predictor of cultural popularity. J Cons Psychol 22(1):154–160. https://doi.org/10.1016/j.jcps.2011.05.001

    Article  Google Scholar 

  31. Bhushan V, Saha G, Lindsen J, Shimojo S, Bhattacharya J (2012) How we choose one over another: predicting trial-by-trial preference decision. PLoS ONE 7(8):e43351. https://doi.org/10.1371/journal.pone.0043351

    Article  Google Scholar 

  32. Bigne E, Chatzipanagiotou K, Ruiz C (2020) Pictorial content, sequence of conflicting online reviews and consumer decision-making: the stimulus-organism-response model revisited. J Bus Res 115:403–416. https://doi.org/10.1016/j.jbusres.2019.11.031

    Article  Google Scholar 

  33. Bigne E, Simonetti A, Ruiz C, Kakaria S (2021) How online advertising competes with user-generated content in TripAdvisor. A neuroscientific approach. J Bus Res 123:279–288. https://doi.org/10.1016/j.jbusres.2020.10.010

    Article  Google Scholar 

  34. Boksem MAS, Smidts A (2015) Brain responses to movie trailers predict individual preferences for movies and their population-wide commercial success. J Market Res 52(4):482–492. https://doi.org/10.1509/jmr.13.0572

    Article  Google Scholar 

  35. Boshoff C (2012) A neurophysiological assessment of consumers’ emotional responses to service recovery behaviors: the impact of ethnic group and gender similarity. J Service Res 15(4):401–413. https://doi.org/10.1177/1094670512453879

    Article  Google Scholar 

  36. Boshoff C (2016) The lady doth protest too much: a neurophysiological perspective on brand tarnishment. J Product Brand Manag 25(2):196–207. https://doi.org/10.1108/JPBM-08-2014-0697

    Article  Google Scholar 

  37. Bosshard SS, Bourke JD, Kunaharan S, Koller M, Walla P (2016) Established liked versus disliked brands: brain activity, implicit associations and explicit responses. Cogent Psychol 3:1176691. https://doi.org/10.1080/23311908.2016.1176691

    Article  Google Scholar 

  38. Braeutigam S, Rose SP, Swithenby SJ, Ambler T (2004) The distributed neuronal systems supporting choice-making in real-life situations: differences between men and women when choosing groceries detected using magnetoencephalography. Eur J Neurosci 20(1):293–302. https://doi.org/10.1111/j.1460-9568.2004.03467.x

    Article  Google Scholar 

  39. Brown C, Randolph AB, Burkhalter JN (2012) The story of taste: using EEGs and self-reports to understand consumer choice. Kennesaw J Undergrad Res 2(1):5. https://doi.org/10.32727/25.2019.5

    Article  Google Scholar 

  40. Buschman TJ, Denovellis EL, Diogo C, Bullock D, Miller EK (2012) Synchronous oscillatory neural ensembles for rules in the prefrontal cortex. Neuron 76(4):838–846. https://doi.org/10.1016/j.neuron.2012.09.029

    Article  Google Scholar 

  41. Cai D, Zhu L, Zhang W, Ding H, Wang A, Lu Y, Jia J (2021) The impact of social crowding on consumers’ online mobile shopping: evidence from behavior and ERPs. Psychol Res Behav Manag 14:319–331. https://doi.org/10.2147/PRBM.S292360

    Article  Google Scholar 

  42. Camarrone F, Van Hulle MM (2019) Measuring brand association strength with EEG: a single-trial N400 ERP study. PLoS ONE 14(6):e0217125. https://doi.org/10.1371/journal.pone.0217125

    Article  Google Scholar 

  43. Caratu M, Cherubino P, Mattiacci A (2018) Application of neuro-marketing techniques to the wine tasting experience. In: Research advancements in national and global business theory and practice, p 299–307

  44. Cartocci G, Caratu M, Modica E, Maglione AG, Rossi D, Cherubino P, Babiloni F (2017) Electroencephalographic, heart rate, and galvanic skin response assessment for an advertising perception study: application to antismoking public service announcements. J Vis Exper 126:e55872. https://doi.org/10.3791/55872

    Article  Google Scholar 

  45. Cartocci G, Cherubino P, Rossi D, Modica E, Maglione AG, di Flumeri G, Babiloni F (2016) Gender and age related effects while watching TV advertisements: an EEG study. Comput Intell Neurosci. https://doi.org/10.1155/2016/3795325

    Article  Google Scholar 

  46. Cartocci G, Modica E, Rossi D, Cherubino P, Maglione AG, Colosimo A, Trettel A, Mancini M, Babiloni F (2018) Neurophysiological measures of the perception of antismoking public service announcements among young population. Front Hum Neurosci. https://doi.org/10.3389/fnhum.2018.00231

    Article  Google Scholar 

  47. Cavanagh JF, Frank MJ (2014) Frontal theta as a mechanism for cognitive control. Trends Cog Sci 18(8):414–421

    Article  Google Scholar 

  48. Cheimariou S, Farmer TA, Gordon JK (2019) Lexical prediction in the aging brain: the effects of predictiveness and congruency on the N400 ERP component. Aging Neuropsychol Cogn 26(5):781–806. https://doi.org/10.1080/13825585.2018.1529733

    Article  Google Scholar 

  49. Chen M, Ma Q, Li M, Lai H, Wang X, Shu L (2010) Cognitive and emotional conflicts of counter-conformity choice in purchasing books online: an event-related potentials study. Biol Psychol 85(3):437–445. https://doi.org/10.1016/j.biopsycho.2010.09.006

    Article  Google Scholar 

  50. Chen Y, Gao Q, Lv Q, Qie N, Ma L (2018) Comparing measurements for emotion evoked by oral care products. Intern J Indust Ergon 66:119–129. https://doi.org/10.1016/j.ergon.2018.02.013

    Article  Google Scholar 

  51. Cherubino P, Modica E, Cartocci G, Maglione AG, Trettel A, Mancini M, Rossi D, Di Flumeri G, Babiloni F (2017) Marketing meets neuroscience: useful insights for gender subgroups during the observation of TV ads. IGI Global. https://doi.org/10.4018/978-1-5225-1028-4.ch008

    Article  Google Scholar 

  52. Cherubino P, Trettel A, Cartocci G, Rossi D, Modica E, Maglione AG, Mancini M, di Flumeri G, Babiloni F (2016) Neuroelectrical indexes for the study of the efficacy of TV advertising stimuli. Sel Issues Exp Econ. https://doi.org/10.1007/978-3-319-28419-4_22

    Article  Google Scholar 

  53. Christoforou C, Papadopoulos TC, Constantinidou F, Theodorou M (2017) Your brain on the movies: a computational approach for predicting box-office performance from viewer’s brain responses to movie trailers. Front Neuroinform 11:72. https://doi.org/10.3389/fninf.2017.00072

    Article  Google Scholar 

  54. Clark KR, Leslie KR, Garcia-Garcia M, Tullman ML (2018) How advertisers can keep mobile users engaged and reduce video-ad blocking best practices for video-ad placement and delivery based on consumer neuroscience measures. J Advert Res 58(3):311–325. https://doi.org/10.2501/JAR-2018-036

    Article  Google Scholar 

  55. Clayton MS, Yeung N, Cohen Kadosh R (2018) The many characters of visual alpha oscillations. Eur J Neurosci 48(7):2498–2508. https://doi.org/10.1111/ejn.13747

    Article  Google Scholar 

  56. Constantinescu M, Orindaru A, Pachitanu A, Rosca L, Caescu S, Orzan MC (2019) Attitude evaluation on using the neuromarketing approach in social media: matching company’s purposes and consumer’s benefits for sustainable business growth. Sustainability 11(24):7094. https://doi.org/10.3390/su11247094

    Article  Google Scholar 

  57. Coronel JC, Federmeier KD (2016) The N400 reveals how personal semantics is processed: insights into the nature and organization of self-knowledge. Neuropsychologia 84:36–43. https://doi.org/10.1016/j.neuropsychologia.2016.01.029

    Article  Google Scholar 

  58. Correa KA, Stone BT, Stikic M, Johnson RR, Berka C (2015) Characterizing donation behavior from psychophysiological indices of narrative experience. Front Neurosci. https://doi.org/10.3389/fnins.2015.00301

    Article  Google Scholar 

  59. Dadebayev D, Goh WW, Tan EX (2021) EEG-based emotion recognition: review of commercial EEG devices and machine learning techniques. Comp Informat Sci. https://doi.org/10.1016/j.jksuci.2021.03.009

    Article  Google Scholar 

  60. Danner L, Haindl S, Joechl M, Duerrschmid K (2014) Facial expressions and autonomous nervous system responses elicited by tasting different juices. Food Res Internat 64:81–90. https://doi.org/10.1016/j.foodres.2014.06.003

    Article  Google Scholar 

  61. Daugherty T, Hoffman E, Kennedy K (2016) Research in reverse: ad testing using an inductive consumer neuroscience approach. J Bus Res. https://doi.org/10.1016/j.jbusres.2015.12.005

    Article  Google Scholar 

  62. Daugherty T, Hoffman E, Kennedy K, Nolan M (2018) Measuring consumer neural activation to differentiate cognitive processing of advertising: revisiting Krugman. Eur J Market 52(1–2):182–198. https://doi.org/10.1108/EJM-10-2017-0657

    Article  Google Scholar 

  63. Deitz GD, Royne MB, Peasley MC, Huang J, Coleman JT (2016) EEG-based measures versus panel ratings predicting social media-based behavioral response to super bowl ads. J Advertis Res 56(2):217–227. https://doi.org/10.2501/JAR-2016-030

    Article  Google Scholar 

  64. Denyer D, Tranfield D (2009) Producing a systematic review. Sage Publications Ltd, Thousand Oaks

    Google Scholar 

  65. Di Gruttola F, Malizia AP, D’Arcangelo S, Lattanzi N, Ricciardi E, Orfei MD (2021) The relation between consumers’ frontal alpha asymmetry, attitude, and investment decision. Front Neurosci. https://doi.org/10.3389/fnins.2020.577978

    Article  Google Scholar 

  66. Diao L, Li W, Zhang W, Ma Q, Jin J (2021) Electroencephalographic theta-band oscillatory dynamics represent attentional bias to subjective preferences in value-based decisions. Psychol Res Behav Manage 14:149–158. https://doi.org/10.2147/PRBM.S292172

    Article  Google Scholar 

  67. Dietterich T (1995) Overfitting and undercomputing in machine learning. ACM Comput Surv 27(3):326–327. https://doi.org/10.1145/212094.212114

    Article  Google Scholar 

  68. Dimpfel W (2015) Neuromarketing: neurocode-tracking in combination with eye-tracking for quantitative objective assessment of TV commercials. J Behav Brain Sci 5(04):137. https://doi.org/10.4236/jbbs.2015.54014

    Article  Google Scholar 

  69. Duque-Hurtado P, Samboni-Rodriguez V, Castro-Garcia M, Montoya-Restrepo LA, Montoya-Restrepo IA (2020) Neuromarketing: its current status and research perspectives. Estud Gerenc 36(157):525–539. https://doi.org/10.18046/j.estger.2020.157.3890

    Article  Google Scholar 

  70. Dulabh M, Vazquez D, Ryding D, Casson A (2018) Measuring consumer engagement in the brain to online interactive shopping environments. In: Claudia M (ed) Augmented reality virtual reality. Springer, Cham, pp 145–165. https://doi.org/10.1007/978-3-319-64027-3_11

    Chapter  Google Scholar 

  71. Eijlers E, Boksem MAS, Smidts A (2020) Measuring neural arousal for advertisements and its relationship with advertising success. Front Neurosci 14:736. https://doi.org/10.3389/fnins.2020.00736

    Article  Google Scholar 

  72. Erbil N, Ungan P (2007) Changes in the alpha and beta amplitudes of the central EEG during the onset, continuation, and offset of long-duration repetitive hand movements. Brain Res 1169:44–56. https://doi.org/10.1016/j.brainres.2007.07.014

    Article  Google Scholar 

  73. Evans D (2002) Systematic reviews of interpretive research: interpretive data synthesis of processed data. Aust J Adv Nurs 20(2):22

    Google Scholar 

  74. Fallani FDV, Astolfi L, Cincotti F, Mattia D, Marciani MG, Gao S, Salinari S, Soranzo R, Colosimo A, Babiloni F (2008) Structure of the cortical networks during successful memory encoding in TV commercials. Clin Neurophysiol 119(10):2231–2237. https://doi.org/10.1016/j.clinph.2008.06.018

    Article  Google Scholar 

  75. Fan B, Li C, Jin J (2020) The brand scandal spillover effect at the Country level: evidence from event-related potentials. Front Neurosci. https://doi.org/10.3389/fnins.2019.01426

    Article  Google Scholar 

  76. Fan B, Zhang Q (2019) Does the aura surrounding healthy-related imported products fade in China? ERP evidence for the country-of-origin stereotype. PLoS ONE 14(5):e0216866. https://doi.org/10.1371/journal.pone.0216866

    Article  MathSciNet  Google Scholar 

  77. Fischer NL, Peres R, Fiorani M (2018) Frontal alpha asymmetry and theta oscillations associated with information sharing intention. Front Behav Neurosci. https://doi.org/10.3389/fnbeh.2018.00166

    Article  Google Scholar 

  78. Flaisch T, Stockburger J, Schupp HT (2008) Affective prime and target picture processing: an ERP analysis of early and late interference effects. Brain Topogr 20(4):183–191. https://doi.org/10.1007/s10548-008-0045-6

    Article  Google Scholar 

  79. Fu H, Ma H, Bian J, Wang C, Zhou J, Ma Q (2019) Don’t trick me: An event-related potentials investigation of how price deception decreases consumer purchase intention. Neurosci Lett 713:134522. https://doi.org/10.1016/j.neulet.2019.134522

    Article  Google Scholar 

  80. Fudali-Czyz A, Ratomska M, Cudo A, Francuz P, Kopis N, Tuznik P (2016) Controlled categorisation processing in brand extension evaluation by Indo-European language speakers. An ERP study. Neurosci Lett 628:30–34. https://doi.org/10.1016/j.neulet.2016.06.005

    Article  Google Scholar 

  81. Gajewski PD, Drizinsky J, Zuelch J, Falkenstein M (2016) ERP correlates of simulated purchase decisions. Front Neurosci 10:360. https://doi.org/10.3389/fnins.2016.00360

    Article  Google Scholar 

  82. Garcia-Madariaga J, Blasco Lopez MF, Burgos IM, Virto NR (2019) Do isolated packaging variables influence consumers’ attention and preferences? Physiol Behav 200:96–103. https://doi.org/10.1016/j.physbeh.2018.04.030

    Article  Google Scholar 

  83. Garcia-Madariaga J, Moya I, Recuero N, Blasco M (2020) Revealing unconscious consumer reactions to advertisements that include visual metaphors. A neurophysiological experiment. Front Psychol 11:760. https://doi.org/10.3389/fpsyg.2020.00760

    Article  Google Scholar 

  84. Garczarek-Bak U, Disterheft A (2018) EEG frontal asymmetry predicts product purchase differently for national brands and private labels. J Neurosci Psychol Econ 11(3):182–195. https://doi.org/10.1037/npe0000094

    Article  Google Scholar 

  85. Gauba H, Kumar P, Roy PP, Singh P, Dogra DP, Raman B (2017) Prediction of advertisement preference by fusing EEG response and sentiment analysis. Neural Netw 92:77–88. https://doi.org/10.1016/j.neunet.2017.01.013

    Article  Google Scholar 

  86. Gkaintatzis A, van der Lubbe R, Karantinou K, Constantinides E (2019) Consumers’ cognitive, emotional and behavioral responses towards background music: an EEG study. Sci Tech Pub. https://doi.org/10.5220/0008346603140318

    Article  Google Scholar 

  87. Golnar-Nik P, Farashi S, Safari M (2019) The application of EEG power for the prediction and interpretation of consumer decision-making: a neuromarketing study. Physiol Behav 207:90–98. https://doi.org/10.1016/j.physbeh.2019.04.025

    Article  Google Scholar 

  88. González-Morales A (2020) Right evaluation of marketing stimuli with neuroscience. An electroencephalography experiment. Comput Hum Behav Rep 2:100030. https://doi.org/10.1016/j.chbr.2020.100030

    Article  Google Scholar 

  89. Goode MR, Iwasa-Madge D (2019) The numbing effect of mortality salience in consumer settings. Psychol Market 36(6):630–641. https://doi.org/10.1002/mar.21201

    Article  Google Scholar 

  90. Gordon R, Ciorciari J, van Laer T (2018) Using EEG to examine the role of attention, working memory, emotion, and imagination in narrative transportation. Eur J Market 52(1–2):92–117. https://doi.org/10.1108/EJM-12-2016-0881

    Article  Google Scholar 

  91. Goto N, Mushtaq F, Shee D, Lim XL, Mortazavi M, Watabe M, Schaefer A (2017) Neural signals of selective attention are modulated by subjective preferences and buying decisions in a virtual shopping task. Biol Psychol 128:11–20

    Article  Google Scholar 

  92. Goto N, Lim XL, Shee D, Hatano A, Khong KW, Buratto LG, Watabe M, Schaefer A (2019) Can brain waves really tell if a product will be purchased? Inferring consumer preferences from single-item brain potentials. Front Integr Neurosci 13:19. https://doi.org/10.3389/fnint.2019.00019

    Article  Google Scholar 

  93. Gou Z, Choudhury N, Benasich AA (2011) Resting frontal gamma power at 16, 24 and 36 months predicts individual differences in language and cognition at 4 and 5 years. Behav Brain Res 220(2):263–270. https://doi.org/10.1016/j.bbr.2011.01.048

    Article  Google Scholar 

  94. Gountas J, Gountas S, Ciorciari J, Sharma P (2019) Looking beyond traditional measures of advertising impact: using neuroscientific methods to evaluate social marketing messages. J Bus Res 105:121–135. https://doi.org/10.1016/j.jbusres.2019.07.011

    Article  Google Scholar 

  95. Gruber T, Tsivilis D, Giabbiconi C, Müller MM (2008) Induced electroencephalogram oscillations during source memory: familiarity is reflected in the gamma band, recollection in the theta band. J Cog Neurosci 20(6):1043–1053. https://doi.org/10.1162/jocn.2008.20068

    Article  Google Scholar 

  96. Guixeres J, Bigné E, Ausin Azofra JM, Alcaniz Raya M, Colomer Granero A, Fuentes Hurtado F, Naranjo Ornedo V (2017) Consumer neuroscience-based metrics predict recall, liking and viewing rates in online advertising. Front Psychol 8:1808. https://doi.org/10.3389/fpsyg.2017.01808

    Article  Google Scholar 

  97. Guo F, Ding Y, Liu W, Liu C, Zhang X (2016) Can eye-tracking data be measured to assess product design?: Visual attention mechanism should be considered. Internat J Industrial Ergon 53:229–235. https://doi.org/10.1016/j.ergon.2015.12.001

    Article  Google Scholar 

  98. Guo F, Li M, Hu M, Li F, Lin B (2019) Distinguishing and quantifying the visual aesthetics of a product: an integrated approach of eye-tracking and EEG. Internat J Industrial Ergon 71:47–56. https://doi.org/10.1016/j.ergon.2019.02.006

    Article  Google Scholar 

  99. Guo F, Ye G, Duffy VG, Li M, Ding Y (2018) Applying eye tracking and electroencephalography to evaluate the effects of placement disclosures on brand responses. J Cons Behav 17(6):519–531. https://doi.org/10.1002/cb.1736

    Article  Google Scholar 

  100. Guo F, Zhang X, Ding Y, Wang X (2016) Recommendation influence: differential neural responses of consumers during shopping online. J Neurosci Psychol Econ 9(1):29–37. https://doi.org/10.1037/npe0000051

    Article  Google Scholar 

  101. Guo G, Elgendi M (2013) A new recommender system for 3D e-commerce: an EEG based approach. J Adv Manag Sci 1(1):61–65

    Article  Google Scholar 

  102. Hakim A, Klorfeld S, Sela T, Friedman D, Shabat-Simon M, Levy DJ (2018) Pathways to consumers’ minds: using machine learning and multiple EEG metrics to increase preference prediction above and beyond traditional measurements. bioRxiv. https://doi.org/10.1101/317073

    Article  Google Scholar 

  103. Hakim A, Klorfeld S, Sela T, Friedman D, Shabat-Simon M, Levy DJ (2020) Machines learn neuromarketing: improving preference prediction from self-reports using multiple EEG measures and machine learning. Internat J Res Market. https://doi.org/10.1016/j.ijresmar.2020.10.005

    Article  Google Scholar 

  104. Hallinger P (2013) A conceptual framework for systematic reviews of research in educational leadership and management. J Educ Admin. https://doi.org/10.1108/09578231311304670

    Article  Google Scholar 

  105. Han C, Lee J, Lim J, Kim Y, Im C (2017) Global electroencephalography synchronization as a new indicator for tracking emotional changes of a group of individuals during video watching. Front Hum Neurosci 11:577. https://doi.org/10.3389/fnhum.2017.00577

    Article  Google Scholar 

  106. Han W, Zhang H, Wang J, Zhao M (2017) Neurological impact of the conflict between brand and product performance on consumer decision process. In: 2017 14th International conference on services systems and services management (Icsssm)

  107. Handy TC, Smilek D, Geiger L, Liu C, Schooler JW (2010) ERP evidence for rapid hedonic evaluation of logos. J Cog Neurosci 22(1):124–138. https://doi.org/10.1162/jocn.2008.21180

    Article  Google Scholar 

  108. Harmon-Jones E, Gable PA, Peterson CK (2010) The role of asymmetric frontal cortical activity in emotion-related phenomena: a review and update. Biol Psychol 84(3):451–462. https://doi.org/10.1016/j.biopsycho.2009.08.010

    Article  Google Scholar 

  109. Harris JM, Ciorciari J, Gountas J (2019) Consumer neuroscience and digital/social media health/social cause advertisement effectiveness. Behav Sci. https://doi.org/10.3390/bs9040042

    Article  Google Scholar 

  110. Herrando C, Jiménez-Martínez J, Martín-De Hoyos M, Constantinides E (2022) Emotional contagion triggered by online consumer reviews: evidence from a neuroscience study. J Retail Cons Serv 67:102973. https://doi.org/10.1016/j.jretconser.2022.102973

    Article  Google Scholar 

  111. Herbes C, Friege C, Baldo D, Mueller K (2015) Willingness to pay lip service? Applying a neuroscience-based method to WTP for green electricity. Energy Policy 87:562–572. https://doi.org/10.1016/j.enpol.2015.10.001

    Article  Google Scholar 

  112. Hewig J (2018) Intentionality in frontal asymmetry research. Psychophysiology 55(1):e12852. https://doi.org/10.1111/psyp.12852

    Article  Google Scholar 

  113. Hoefer D, Handel M, Müller K, Hammer TR (2016) The buying brain, screens, and social media; vision of the future; electroencephalographic study showing that tactile stimulation by fabrics of different qualities elicit graded event-related potentials. Skin Res Technol 22(4):219. https://doi.org/10.1002/9781119200079.ch17

    Article  Google Scholar 

  114. Hoefer D, Handel M, Muller KM, Hammer TR (2016) Electroencephalographic study showing that tactile stimulation by fabrics of different qualities elicit graded event-related potentials. Skin Res Technol 22(4):470–478. https://doi.org/10.1111/srt.12288

    Article  Google Scholar 

  115. Horska E, Bercik J, Krasnodebski A, Matysik-Pejas R, Bakayova H (2016) Innovative approaches to examining consumer preferences when choosing wines. Agric Econ 62(3):124–133. https://doi.org/10.17221/290/2015-AGRICECON

    Article  Google Scholar 

  116. Horská E, Berčík J (2014) The influence of light on consumer behavior at the food market. J Food Prod Market 20(4):429–440. https://doi.org/10.1080/10454446.2013.838531

    Article  Google Scholar 

  117. Hsu L, Chen Y (2020) Neuromarketing, subliminal advertising, and hotel selection: an EEG study. Australas Market J (AMJ) 28(4):200–208. https://doi.org/10.1016/j.ausmj.2020.04.009

    Article  Google Scholar 

  118. Hsu M (2017) Neuromarketing: inside the mind of the consumer. Calif Manag Rev 59(4):5–22. https://doi.org/10.1177/0008125617720208

    Article  Google Scholar 

  119. Hsu W (2017) An integrated-mental brainwave system for analyses and judgments of consumer preference. Telemat Inform 34(5):518–526. https://doi.org/10.1016/j.tele.2016.11.002

    Article  Google Scholar 

  120. Hungenberg E, Slavich M, Bailey A, Sawyer T (2020) Examining minor league baseball spectator nostalgia: a neuroscience perspective. Sport Manag Rev 23(5):824–837. https://doi.org/10.1016/j.smr.2020.04.001

    Article  Google Scholar 

  121. Inanaga K (1998) Frontal midline theta rhythm and mental activity. Psychiatry Clin Neurosci 52(6):555–566. https://doi.org/10.1046/j.1440-1819.1998.00452.x

    Article  Google Scholar 

  122. Janić M, Ćirović M, Dimitriadis N, Jovanović Dimitriadis N, Alevizou P (2022) Neuroscience and CSR: using EEG for assessing the effectiveness of branded videos related to environmental issues. Sustainability 14(3):1347. https://doi.org/10.3390/su14031347

    Article  Google Scholar 

  123. Jeong YE, Kim JH (2017) A study of consumer’s emotional response on musical stimulus through EEG analysis: based on color perception of consumers. Korean J Bus Adm 30(12):2147–2172. https://doi.org/10.18032/kaaba.2017.30.9.2147

    Article  Google Scholar 

  124. Jia J, Dou X, Liang M, Yu H (2018) Environmental-friendly eco-labeling matters: evidences from an ERPs study. Front Hum Neurosci. https://doi.org/10.3389/fnhum.2018.00417

    Article  Google Scholar 

  125. Jin J, Wang C, Yu L, Ma Q (2015) Extending or creating a new brand: evidence from a study on event-related potentials. NeuroReport 26(10):572–577. https://doi.org/10.1097/WNR.0000000000000390

    Article  Google Scholar 

  126. Jin J, Zhang W, Chen M (2017) How consumers are affected by product descriptions in online shopping: event-related potentials evidence of the attribute framing effect. Neurosci Res 125:21–28. https://doi.org/10.1016/j.neures.2017.07.006

    Article  Google Scholar 

  127. Jin KIMHYUN, Cho S (2019) The effect of korean and chinese consumers’ expectancy disconfirmation in restaurants on complaining behavior: the moderating effect of neuroticism. J Product Res 37(5):181–192

    Google Scholar 

  128. Jing K, Mei Y, Song Z, Wang H, Shi R (2019) How do price and quantity promotions affect hedonic purchases? An ERPs study. Front Neurosci. https://doi.org/10.3389/fnins.2019.00526

    Article  Google Scholar 

  129. Jones WJ, Childers TL, Jiang Y (2012) The shopping brain: math anxiety modulates brain responses to buying decisions. Biol Psychol 89(1):201–213. https://doi.org/10.1016/j.biopsycho.2011.10.011

    Article  Google Scholar 

  130. Jordão IL, Souza MT, Oliveira JH, Giraldi JD (2017) Neuromarketing applied to consumer behaviour: an integrative literature review between 2010 and 2015. Internat J Bus Forecast Market Intell 3(3):270–288

    Google Scholar 

  131. Junghoefer M, Kissler J, Schupp HT, Putsche C, Elling L, Dobel C (2010) A fast neural signature of motivated attention to consumer goods separates the sexes. Front Hum Neurosci 4:179. https://doi.org/10.3389/fnhum.2010.00179

    Article  Google Scholar 

  132. Kacaniova M, Vargova V (2017) Electroencephalography as a tool of advertising research in the context of Mac model. Eur J Sci Theol 13(6):145–155

    Google Scholar 

  133. Käthner I, Wriessnegger SC, Müller-Putz GR, Kübler A, Halder S (2014) Effects of mental workload and fatigue on the P300, alpha and theta band power during operation of an ERP (P300) brain–computer interface. Biol Psychol 102:118–129. https://doi.org/10.1016/j.biopsycho.2014.07.014

    Article  Google Scholar 

  134. Kenning P, Plassmann H, Ahlert D (2007) Applications of functional magnetic resonance imaging for market research. Qualitat Market Res Internat J. https://doi.org/10.1108/13522750710740817

    Article  Google Scholar 

  135. Khan KS, Kunz R, Kleijnen J, Antes G (2003) Five steps to conducting a systematic review. J Royal Soc Med 96(3):118–121. https://doi.org/10.1108/13522750710740817

    Article  Google Scholar 

  136. Khurana V, Gahalawat M, Kumar P, Roy PP, Dogra DP, Scheme E, Soleymani M (2021) A survey on neuromarketing using EEG signals. IEEE Trans Cogn Dev Syst 13(4):732–749. https://doi.org/10.1109/TCDS.2021.3065200

    Article  Google Scholar 

  137. Khushaba RN, Greenacre L, Al-Timemy A, Al-Jumaily A (2015) Event-related potentials of consumer preferences. Proced Comp Sci 76:68–73. https://doi.org/10.1016/j.procs.2015.12.277

    Article  Google Scholar 

  138. Khushaba RN, Greenacre L, Kodagoda S, Louviere J, Burke S, Dissanayake G (2012) Choice modeling and the brain: a study on the Electroencephalogram (EEG) of preferences. Expert Syst Appl 39(16):12378–12388. https://doi.org/10.1016/j.eswa.2012.04.084

    Article  Google Scholar 

  139. Khushaba RN, Wise C, Kodagoda S, Louviere J, Kahn BE, Townsend C (2013) Consumer neuroscience: assessing the brain response to marketing stimuli using electroencephalogram (EEG) and eye tracking. Expert Syst Appl 40(9):3803–3812. https://doi.org/10.1016/j.eswa.2012.12.095

    Article  Google Scholar 

  140. Kim AE, Oines L, Miyake A (2018) Individual differences in verbal working memory underlie a tradeoff between semantic and structural processing difficulty during language comprehension: an ERP investigation. J Exp Psychol Learn Mem Cog 44(3):406. https://doi.org/10.1037/xlm0000457

    Article  Google Scholar 

  141. Kim Y, Park K, Kim Y, Yang W, Han D, Kim W (2020) The impact of visual art and high affective arousal on heuristic decision-making in consumers. Front Psychol 11:565829. https://doi.org/10.3389/fpsyg.2020.565829

    Article  Google Scholar 

  142. Klimesch W (1999) EEG alpha and theta oscillations reflect cognitive and memory performance: a review and analysis. Brain Res Rev 29(2–3):169–195. https://doi.org/10.1016/S0165-0173(98)00056-3

    Article  Google Scholar 

  143. Klimesch W (2012) Alpha-band oscillations, attention, and controlled access to stored information. Trends Cog Sci 16(12):606–617. https://doi.org/10.1016/j.tics.2012.10.007

    Article  Google Scholar 

  144. Knutson B, Genevsky A (2018) Neuroforecasting aggregate choice. Curr Direct Psychol Sci 27(2):110–115. https://doi.org/10.1177/0963721417737877

    Article  Google Scholar 

  145. Kong W, Zhao X, Hu S, Vecchiato G, Babiloni F (2013) Electronic evaluation for video commercials by impression index. Cogn Neurodyn 7(6):531–535. https://doi.org/10.1007/s11571-013-9255-z

    Article  Google Scholar 

  146. Kong W, Zhang X, Wang L, Fan Q, Dai Y, Miao Y (2019) Evaluation of product placement with attention on eye-tracking and EEG. J Beijing Instit Technol 39:792–793

    Google Scholar 

  147. Krugman DM, Fox RJ, Fletcher JE, Rojas TH (1994) Do adolescents attend to warnings in cigarette advertising? An eye-tracking approach. J Advert Res 34(6):39–53

    Google Scholar 

  148. Kuan KKY, Zhong Y, Chau PYK (2014) Informational and normative social influence in group-buying: evidence from self-reported and EEG data. J Manage Inf Syst 30(4):151–178. https://doi.org/10.2753/MIS0742-1222300406

    Article  Google Scholar 

  149. Kühn S, Strelow E, Gallinat J (2016) Multiple “buy buttons” in the brain: forecasting chocolate sales at point-of-sale based on functional brain activation using fMRI. Neuroimage 136:122–128. https://doi.org/10.1016/j.neuroimage.2016.05.021

    Article  Google Scholar 

  150. Kumagai Y, Arvaneh M, Tanaka T (2017) Familiarity affects entrainment of EEG in music listening. Front Hum Neurosci. https://doi.org/10.3389/fnhum.2017.00384

    Article  Google Scholar 

  151. Kumar S, Yadava M, Roy PP (2019) Fusion of EEG response and sentiment analysis of products review to predict customer satisfaction. Informat Fusion 52:41–52. https://doi.org/10.1016/j.inffus.2018.11.001

    Article  Google Scholar 

  152. Kutas M, Federmeier KD (2011) Thirty years and counting: finding meaning in the N400 component of the event-related brain potential (ERP). Annu Rev Psychol 62:621–647. https://doi.org/10.1146/annurev.psych.093008.131123

    Article  Google Scholar 

  153. Kuzava S, Frost A, Perrone L, Kang E, Lindhiem O, Bernard K (2020) Adult processing of child emotional expressions: a meta-analysis of ERP studies. Dev Psychol 56(6):1170. https://doi.org/10.1037/dev0000928

    Article  Google Scholar 

  154. Kytö E, Bult H, Aarts E, Wegman J, Ruijschop RM, Mustonen S (2019) Comparison of explicit vs. implicit measurements in predicting food purchases. Food Qual Prefer 78:103733. https://doi.org/10.1016/j.foodqual.2019.103733

    Article  Google Scholar 

  155. Lajante MMP, Droulers O, Amarantini D (2017) How reliable are “State-of-the-Art” facial EMG processing methods? Guidelines for improving the assessment of emotional valence in advertising research. J Advert Res 57(1):28–37. https://doi.org/10.2501/JAR-2017-011

    Article  Google Scholar 

  156. Lamy D, Salti M, Bar-Haim Y (2009) Neural correlates of subjective awareness and unconscious processing: an ERP study. J Cog Neurosci 21(7):1435–1446. https://doi.org/10.1162/jocn.2009.21064

    Article  Google Scholar 

  157. Laurence J, Gerhold MM (2016) Saving the day: the relationship between emotion and purchase intent in television advertising. HeadSpace Neuromarket

  158. Leanza F (2017) Consumer neuroscience: the traditional and VR TV commercial. Neuropsychol Trends 21:81–90. https://doi.org/10.7358/neur-2017-021-lean

    Article  Google Scholar 

  159. Lee EJ, Shin HJ, Yang S, Kwon G, Suh M (2013) The conscious choice of homo evolutis: can fronto-parietal EEG activations predict the consumer choice of sustainable products? Korean Manag Rev 42(3):805–821

    Article  Google Scholar 

  160. Lee E (2016) Empathy can increase customer equity related to pro-social brands. J Bus Res 69(9):3748–3754. https://doi.org/10.1016/j.jbusres.2015.05.018

    Article  Google Scholar 

  161. Lee E, Kwon G, Shin HJ, Yang S, Lee S, Suh M (2014) The spell of green: can frontal EEG activations identify green consumers? J Bus Ethics 122(3):511–521. https://doi.org/10.1007/s10551-013-1775-2

    Article  Google Scholar 

  162. Lee H (2016) Difference in P300 latency by levels of involvement to sport: Interdisciplinary approach to sport consumer behavior using embodied cognition theory and EEG/ERP technique. Korea Sports Ind Manag J 21(6):45–56

    Google Scholar 

  163. Lee N, Broderick AJ, Chamberlain L (2007) What is “neuromarketing”? A discussion and agenda for future research. Internat J Psychophysiol 63(2):199–204. https://doi.org/10.1016/j.ijpsycho.2006.03.007

    Article  Google Scholar 

  164. Li B, Wang Y, Wang K (2016) Data fusion and analysis techniques of neuromarketing. WIT Transact Eng Sci 113:396–404. https://doi.org/10.2495/IWAMA150461

    Article  Google Scholar 

  165. Li Z, Bu G (2013) Mechanism of Consumer's Brand Loyalty Based on Neuromarketing. In: 2013 3rd International Conference on Education and Education Management (Eem 2013), 25, p 235–238

  166. Liao W, Zhang Y, Peng X (2019) Neurophysiological effect of exposure to gossip on product endorsement and willingness-to-pay. Neuropsychologia 132:107123. https://doi.org/10.1016/j.neuropsychologia.2019.107123

    Article  Google Scholar 

  167. Lietz P (2010) Research into questionnaire design: a summary of the literature. Int J Market Res 52(2):249–272. https://doi.org/10.2501/5147078530920120X

    Article  Google Scholar 

  168. Lim WM (2018) Demystifying neuromarketing. J Bus Res 91:205–220. https://doi.org/10.1016/j.jbusres.2018.05.036

    Article  Google Scholar 

  169. Lin H, Saunders B, Hutcherson CA, Inzlicht M (2018) Midfrontal theta and pupil dilation parametrically track subjective conflict (but also surprise) during intertemporal choice. Neuroimage 172:838–852. https://doi.org/10.1016/j.neuroimage.2017.10.055

    Article  Google Scholar 

  170. Luan J, Yao Z, Bai Y (2017) How social ties influence consumer: evidence from event-related potentials. PLoS ONE 12(1):e0169508. https://doi.org/10.1371/journal.pone.0169508

    Article  Google Scholar 

  171. Lucchiari C, Pravettoni G (2012) The effect of brand on EEG modulation a study on mineral water. Swiss J Psychol 71(4):199–204. https://doi.org/10.1024/1421-0185/a000088

    Article  Google Scholar 

  172. Luck SJ (2005) Ten simple rules for designing and interpreting ERP experiments. Event-related potentials: a methods handbook, 4

  173. Ma G, Zhuang X (2021) Nutrition label processing in the past 10 years: contributions from eye tracking approach. Appetite 156:104859. https://doi.org/10.1016/j.appet.2020.104859

    Article  Google Scholar 

  174. Ma H, Mo Z, Zhang H, Wang C, Fu H (2018) The temptation of zero price: event-related potentials evidence of how price framing influences the purchase of bundles. Front Neurosci. https://doi.org/10.3389/fnins.2018.00251

    Article  Google Scholar 

  175. Ma Q, Abdeljelil HM, Hu L (2019) The influence of the consumer ethnocentrism and cultural familiarity on brand preference: evidence of event-related potential (ERP). Fronti Hum Neurosci 13:220. https://doi.org/10.3389/fnhum.2019.00220

    Article  Google Scholar 

  176. Ma Q, Zhang L, Wang M (2018) “You win, you buy”-how continuous win effect influence consumers’ price perception: an ERP study. Front Neurosci 12:691. https://doi.org/10.3389/fnins.2018.00691

    Article  Google Scholar 

  177. Ma Q, Wang C, Wang X (2014) Two-stage categorization in brand extension evaluation: electrophysiological time course evidence. PLoS ONE 9(12):e114150. https://doi.org/10.1371/journal.pone.0114150

    Article  Google Scholar 

  178. Ma Q, Wang M, Hu L, Zhang L, Hua Z (2021) A novel recurrent neural network to classify EEG signals for customers’ decision-making behavior prediction in brand extension scenario. Front Hum Neurosci. https://doi.org/10.3389/fnhum.2021.610890

    Article  Google Scholar 

  179. Ma Q, Wang X, Dai S, Shu L (2007) Event-related potential N270 correlates of brand extension. NeuroReport 18(10):1031–1034. https://doi.org/10.1097/WNR.0b013e3281667d59

    Article  Google Scholar 

  180. Ma Q, Wang X, Shu L, Dai S (2008) P300 and categorization in brand extension. Neurosci Letters 431(1):57–61. https://doi.org/10.1016/j.neulet.2007.11.022

    Article  Google Scholar 

  181. Ma Y, Jin J, Yu W, Zhang W, Xu Z, Ma Q (2018) How is the neural response to the design of experience goods related to personalized preference? An implicit view. Front Neurosci 12:760. https://doi.org/10.3389/fnins.2018.00760

    Article  Google Scholar 

  182. Mahamad NA, Amin M, Mikami O (2019) Evaluating neuromarketing technique on consumer satisfaction using EEG imaging. J Adv Manufact Technol 13(2):2

    Google Scholar 

  183. Makeig S (1993) Auditory event-related dynamics of the EEG spectrum and effects of exposure to tones. Electroencephal Clin Neurophysiol 86(4):283–293. https://doi.org/10.1016/0013-4694(93)90110-H

    Article  Google Scholar 

  184. Makeig S, Bell A, Jung T, Sejnowski TJ (1995) Independent component analysis of electroencephalographic data. Adv Neural Inform Process Syst, 8

  185. Mashrur FR, Rahman KM, Miya MT, Vaidyanathan R, Anwar SF, Sarker F, Mamun KA (2022) BCI-based consumers’ choice prediction from EEG signals: an intelligent neuromarketing system. Front Hum Neurosci. https://doi.org/10.3389/fnhum.2022.861270

    Article  Google Scholar 

  186. Mashrur FR, Rahman KM, Miya MT, Vaidyanathan R, Anwar SF, Sarker F, Mamun KA (2022) An Intelligent Neuromarketing System for Predicting Consumers’ Future Choice from Electroencephalography Signals. Physiol Behav. https://doi.org/10.1016/j.physbeh.2022.113847

    Article  Google Scholar 

  187. McDuff D, El Kaliouby R, Cohn JF, Picard RW (2015) Predicting ad liking and purchase intent: large-scale analysis of facial responses to ads. IEEE Transact Affect Computi 6(3):223–235. https://doi.org/10.1109/TAFFC.2014.2384198

    Article  Google Scholar 

  188. Mehrabian A (1995) Framework for a comprehensive description and measurement of emotional states. Genetic Soc Gen Psychol Monogr 121:339

    Google Scholar 

  189. Mengual-Recuerda A, Tur-Viñes V, Juárez-Varón D (2020) Neuromarketing in Haute cuisine gastronomic experiences. Front Psychol 11:1772. https://doi.org/10.3389/fpsyg.2020.01772

    Article  Google Scholar 

  190. Misselhorn J, Friese U, Engel AK (2019) Frontal and parietal alpha oscillations reflect attentional modulation of cross-modal matching. Sci Rep 9(1):1–11. https://doi.org/10.1038/s41598-019-41636-w

    Article  Google Scholar 

  191. Modica E, Cartocci G, Rossi D, Levy ACM, Cherubino P, Maglione AG, Di Flumeri G, Mancini M, Montanari M, Perrotta D, Di Feo P, Vozzi A, Ronca V, Arico P, Babiloni F (2018) Neurophysiological responses to different product experiences. Comput Intell Neurosci 2018:9616301. https://doi.org/10.1155/2018/9616301

    Article  Google Scholar 

  192. Modica E, Rossi D, Cartocci G, Perrotta D, Di Feo P, Mancini M, Aricò P, Inguscio BMS, Babiloni F (2018) Neurophysiological profile of antismoking campaigns. Comput Intell Neurosci. https://doi.org/10.1155/2018/9721561

    Article  Google Scholar 

  193. Moran RJ, Pinotsis DA, Friston KJ (2013) Neural masses and fields in dynamic causal modeling. Front Comp Neurosci 7:57

    Google Scholar 

  194. Morey AC (2017) Memory for positive and negative political TV ads: the role of partisanship and gamma power. Polit Commun 34(3):404–423. https://doi.org/10.1080/10584609.2016.1266534

    Article  Google Scholar 

  195. Moya I, García-Madariaga J, Blasco M (2020) What can neuromarketing tell us about food packaging? Foods. https://doi.org/10.3390/foods9121856

  196. Nie A, Yu Y (2021) External (versus internal) facial features contribute most to repetition priming in facial recognition: ERP evidence. Percept Motor Skills 128(1):15–47. https://doi.org/10.1177/0031512520957150

    Article  Google Scholar 

  197. Niedermeyer E (2003) The clinical relevance of EEG interpretation. Clin Electroencephal 34(3):93–98. https://doi.org/10.1177/155005940303400303

    Article  Google Scholar 

  198. Nittono H, Watari K (2017) Effects of food sampling on brain potential responses to food branding. Psychologia 60(1):3–15. https://doi.org/10.2117/psysoc.2017.3

    Article  Google Scholar 

  199. Nomura T, Mitsukura Y (2015) EEG-based detection of TV commercials effects. Proced Comp Sci 60:131–140. https://doi.org/10.1016/j.procs.2015.08.112

    Article  Google Scholar 

  200. Nunez PL, Srinivasan R, Westdorp AF, Wijesinghe RS, Tucker DM, Silberstein RB, Cadusch PJ (1997) EEG coherency: I: statistics, reference electrode, volume conduction, Laplacians, cortical imaging, and interpretation at multiple scales. Electroencephal Clin Neurophysiol 103(5):499–515. https://doi.org/10.1016/S0013-4694(97)00066-7

    Article  Google Scholar 

  201. Ohme R, Matukin M (2012) A small frog that makes a big difference: brain wave testing of TV advertisements. IEEE Pulse 3(3):28–33. https://doi.org/10.1109/MPUL.2012.2189169

    Article  Google Scholar 

  202. Ohme R, Matukin M, Pacula-Lesniak B (2011) Biometric measures for interactive advertising research. J Interact Advert 11(2):60–72. https://doi.org/10.1080/15252019.2011.10722185

    Article  Google Scholar 

  203. Ohme R, Reykowska D, Wiener D, Choromanska A (2009) Analysis of neurophysiological reactions to advertising stimuli by means of EEG and galvanic skin response measures. J Neurosci Psychol Econ 2(1):21. https://doi.org/10.1037/a0015462

    Article  Google Scholar 

  204. Ohme R, Reykowska D, Wiener D, Choromanska A (2010) Application of frontal EEG asymmetry to advertising research. J Econ Psychol 31(5):785–793. https://doi.org/10.1016/j.joep.2010.03.008

    Article  Google Scholar 

  205. Ozkara BY, Bagozzi R (2021) The use of event related potentials brain methods in the study of Conscious and unconscious consumer decision making processes. J Retail Cons Serv 58:102202. https://doi.org/10.1016/j.jretconser.2020.102202

    Article  Google Scholar 

  206. Pandey P, Swarnkar R, Kakaria S, Miyapuram KP (2020) Understanding consumer preferences for movie trailers from EEG using machine learning. arXiv Preprint. https://arxiv.org/abs/2007.10756

  207. Pennanen K, Närväinen J, Vanhatalo S, Raisamo R, Sozer N (2020) Effect of virtual eating environment on consumers’ evaluations of healthy and unhealthy snacks. Food Qual Prefer 82:103871. https://doi.org/10.1016/j.foodqual.2020.103871

    Article  Google Scholar 

  208. Pfurtscheller G (1981) Central beta rhythm during sensorimotor activities in man. Electroencephalogr Clin Neurophysiol 51(3):253–264. https://doi.org/10.1016/0013-4694(81)90139-5

    Article  Google Scholar 

  209. Pfurtscheller G, Andrew C (1999) Event-related changes of band power and coherence: methodology and interpretation. J Clin Neurophysiol 16(6):512

    Article  Google Scholar 

  210. Phutela N, Sreevathsan K, Krupa BN (2022) Intelligent analysis of EEG signals to assess consumer decisions: a Study on Neuromarketing. arXiv. https://arxiv.org/abs/2206.07484

  211. Pileliene L, Grigaliunaite V (2017) The effect of female celebrity spokesperson in FMCG advertising: neuromarketing approach. J Consum Mark 34(3):202–213. https://doi.org/10.1108/JCM-02-2016-1723

    Article  Google Scholar 

  212. Pileliene L, Grigaliunaite V (2017) Relationship between Spokesperson’s gender and advertising color temperature in a framework of advertising effectiveness. Sci Annals Econ Bus 64:1–13. https://doi.org/10.1515/saeb-2017-0036

    Article  Google Scholar 

  213. Plassmann H, Ramsøy TZ, Milosavljevic M (2012) Branding the brain: a critical review and outlook. J Cons Psychol 22(1):18–36. https://doi.org/10.1016/j.jcps.2011.11.010

    Article  Google Scholar 

  214. Poels K, Dewitte S (2006) How to capture the heart? Reviewing 20 years of emotion measurement in advertising. J Advert Res 46(1):18–37

    Article  Google Scholar 

  215. Pozharliev R, Verbeke WJMI, Van Strien JW, Bagozzi RP (2015) Merely being with you increases my attention to luxury products: using EEG to understand consumers’ emotional experience with luxury branded products. J Market Res 52(4):546–558. https://doi.org/10.1509/jmr.13.0560

    Article  Google Scholar 

  216. Raiesdana S, Mousakhani M (2022) An EEG-based neuromarketing approach for analyzing the preference of an electric car. Comput Intel Neurosci. https://doi.org/10.1155/2022/9002101

    Article  Google Scholar 

  217. Ramsøy TZ, Skov M, Christensen MK, Stahlhut C (2018) Frontal brain asymmetry and willingness to pay. Front Neurosci 12:138. https://doi.org/10.3389/fnins.2018.00138

    Article  Google Scholar 

  218. Ramsøy TZ, Noela M, Michael I (2019) A consumer neuroscience study of conscious and subconscious destination preference. Sci Rep 9:1–8. https://doi.org/10.1038/s41598-019-51567-1

    Article  Google Scholar 

  219. Ravaja N, Somervuori O, Salminen M (2013) Predicting purchase decision: the role of hemispheric asymmetry over the frontal cortex. J Neurosci Psychol Econ 6(1):1–13. https://doi.org/10.1037/a0029949

    Article  Google Scholar 

  220. Rawnaque FS, Rahman KM, Anwar SF, Vaidyanathan R, Chau T, Sarker F, Mamun KA (2020) Technological advancements and opportunities in neuromarketing: a systematic review. Brain Informat 7(1):1–19. https://doi.org/10.1186/s40708-020-00109-x

    Article  Google Scholar 

  221. Rayner K, Castelhano MS (2008) Eye movements during reading, scene perception, visual search, and while looking at print advertisements. Taylor and Francis Group, Washington

    Google Scholar 

  222. Robaina-Calderín L, Martín-Santana J (2021) A review of research on neuromarketing using content analysis: key approaches and new avenues. Cog Neurodyn 15(6):923–938. https://doi.org/10.1007/s11571-021-09693-y

    Article  Google Scholar 

  223. Roberts H, Soto V, Tyson-Carr J, Kokmotou K, Cook S, Fallon N, Giesbrecht T, Stancak A (2018) Tracking economic value of products in natural settings: a wireless EEG study. Front Neurosci. https://doi.org/10.3389/fnins.2018.00910

    Article  Google Scholar 

  224. Robinson R (2004) fMRI beyond the clinic: will it ever be ready for prime time? PLoS Biol 2(6):e150. https://doi.org/10.1371/journal.pbio.0020150

    Article  Google Scholar 

  225. Roelofs R, Shankar V, Recht B, Fridovich-Keil S, Hardt M, Miller J, Schmidt L (2019) A meta-analysis of overfitting in machine learning. Adv Neural Inform Proces Syst 10:32. https://doi.org/10.5555/3454287.3455110

    Article  Google Scholar 

  226. Rosenbaum MS, Contreras Ramirez G, Matos N (2019) A neuroscientific perspective of consumer responses to retail greenery. Service Indust J 39(15–16):1034–1045. https://doi.org/10.1080/02642069.2018.1487406

    Article  Google Scholar 

  227. Rothschild ML, Hyun YJ, Reeves B, Thorson E, Goldstein R (1988) Hemispherically lateralized EEG as a response to television commercials. J Cons Res 15(2):185–198. https://doi.org/10.1086/209156

    Article  Google Scholar 

  228. Rothschild ML, Thorson E, Reeves B, Hirsch JE, Goldstein R (1986) EEG activity and the processing of television commercials. Commun Res 13(2):182–220. https://doi.org/10.1177/009365086013002003

    Article  Google Scholar 

  229. Rothschild ML, Hyun YJ (1990) Predicting memory for components of Tv commercials from EEG. J Cons Res 16(4):472–478. https://doi.org/10.1086/209232

    Article  Google Scholar 

  230. Roux F, Wibral M, Mohr HM, Singer W, Uhlhaas PJ (2012) Gamma-band activity in human prefrontal cortex codes for the number of relevant items maintained in working memory. J Neurosci 32(36):12411–12420. https://doi.org/10.1523/JNEUROSCI.0421-12.2012

    Article  Google Scholar 

  231. Royo M, Chulvi V, Mulet E, Galan J (2018) Users’ reactions captured by means of an EEG headset on viewing the presentation of sustainable designs using verbal narrative. Eur J Market 52(1–2):159–181. https://doi.org/10.1108/EJM-12-2016-0837

    Article  Google Scholar 

  232. Russo V, Songa G, Milani Marin LE, Balzaretti CM, Tedesco DEA (2020) Novel Food-Based Product Communication: A Neurophysiological Study. Nutrients. https://doi.org/10.3390/nu12072092

    Article  Google Scholar 

  233. Sapountzis P, Gregoriou GG (2018) Neural signatures of attention: insights from decoding population activity patterns. Front Biosci 23:221–246

    Article  Google Scholar 

  234. Sauseng P, Griesmayr B, Freunberger R, Klimesch W (2010) Control mechanisms in working memory: a possible function of EEG theta oscillations. Neurosci Biobehav Rev 34(7):1015–1022. https://doi.org/10.1016/j.neubiorev.2009.12.006

    Article  Google Scholar 

  235. Schacter DL (1977) EEG theta waves and psychological phenomena: a review and analysis. Biol Psychol 5(1):47–82. https://doi.org/10.1016/0301-0511(77)90028-X

    Article  Google Scholar 

  236. Schaefer A, Buratto LG, Goto N, Brotherhood EV (2016) The feedback-related negativity and the P300 brain potential are sensitive to price expectation violations in a virtual shopping task. PLoS ONE 11(9):e0163150. https://doi.org/10.1371/journal.pone.0163150

    Article  Google Scholar 

  237. Shestyuk AY, Kasinathan K, Karapoondinott V, Knight RT, Gurumoorthy R (2019) Individual EEG measures of attention, memory, and motivation predict population level TV viewership and Twitter engagement. PLoS ONE 14(3):e0214507. https://doi.org/10.1371/journal.pone.0214507

    Article  Google Scholar 

  238. Schneider S, Strüder HK (2012) EEG: Theoretical background and practical aspects. In: Henning B, Charles HH, Lukas S, Heiko KS (eds) Functional neuroimaging in exercise and sport sciences. Springer, New York, pp 197–212

    Chapter  Google Scholar 

  239. Schoen F, Lochmann M, Prell J, Herfurth K, Rampp S (2018) Neuronal correlates of product feature attractiveness. Front Behav Neurosci. https://doi.org/10.3389/fnbeh.2018.00147

    Article  Google Scholar 

  240. Schöne B, Köster M, Gruber T (2018) Coherence in general and personal semantic knowledge: functional differences of the posterior and centro-parietal N400 ERP component. Exp Brain Res 236(10):2649–2660. https://doi.org/10.1007/s00221-018-5324-1

    Article  Google Scholar 

  241. Schupp HT, Öhman A, Junghöfer M, Weike AI, Stockburger J, Hamm AO (2004) The facilitated processing of threatening faces: an ERP analysis. Emotion 4(2):189. https://doi.org/10.1037/1528-3542.4.2.189

    Article  Google Scholar 

  242. Schupp HT, Schmälzle R, Flaisch T, Weike AI, Hamm AO (2012) Affective picture processing as a function of preceding picture valence: an ERP analysis. Biol Psychol 91(1):81–87. https://doi.org/10.1016/j.biopsycho.2012.04.006

    Article  Google Scholar 

  243. Senecal S, Fredette M, Leger P, Courtemanche F, Riedl R (2015) Consumers’ cognitive lock-in on websites: evidence from a neurophysiological study. J Internet Commer 14(3):277–293. https://doi.org/10.1080/15332861.2015.1028249

    Article  Google Scholar 

  244. Senior C, Lee N (2013) The state of the art in organizational cognitive neuroscience: the therapeutic gap and possible implications for clinical practice. Front Hum Neurosci 7:808. https://doi.org/10.3389/fnhum.2013.00808

    Article  Google Scholar 

  245. Shaari N, Syafiq M, Amin M, Mikami O (2019) Electroencephalography (EEG) application in neuromarketing-exploring the subconscious mind. J Adv Manuf Tech 13(2):2

    Google Scholar 

  246. Shang Q, Pei G, Jia J, Zhang W, Wang Y, Wang X (2018) ERP evidence for consumer evaluation of copycat brands. PLoS ONE 13(2):e0191475. https://doi.org/10.1371/journal.pone.0191475

    Article  Google Scholar 

  247. Shang Q, Jin J, Pei G, Wang C, Wang X, Qiu J (2020) Low-order webpage layout in online shopping facilitates purchase decisions: evidence from event-related potentials. Psychol Res Behav Manage 13:29–39. https://doi.org/10.2147/PRBM.S238581

    Article  Google Scholar 

  248. Shang Q, Pei G, Dai S, Wang X (2017) Logo effects on brand extension evaluations from the electrophysiological perspective. Front Neurosci 11:113. https://doi.org/10.3389/fnins.2017.00113

    Article  Google Scholar 

  249. Shen Y, Shan W, Luan J (2018) Influence of aggregated ratings on purchase decisions: an event-related potential study. Eur J Market 52(1–2):147–158. https://doi.org/10.1108/EJM-12-2016-0871

    Article  Google Scholar 

  250. Silverstein BH, Snodgrass M, Shevrin H, Kushwaha R (2015) P3b, consciousness, and complex unconscious processing. Cortex 73:216–227. https://doi.org/10.1016/j.cortex.2015.09.004

    Article  Google Scholar 

  251. Slanzi G, Balazs JA, Velasquez JD (2017) Combining eye tracking, pupil dilation and EEG analysis for predicting web users click intention. Inform Fusion 35:51–57. https://doi.org/10.1016/j.inffus.2016.09.003

    Article  Google Scholar 

  252. Smith EE, Reznik SJ, Stewart JL, Allen JJ (2017) Assessing and conceptualizing frontal EEG asymmetry: an updated primer on recording, processing, analyzing, and interpreting frontal alpha asymmetry. Internat J Psychophysiol 111:98–114. https://doi.org/10.1016/j.ijpsycho.2016.11.005

    Article  Google Scholar 

  253. Smith ME, Gevins A (2004) Attention and brain activity while watching television: components of viewer engagement. Media Psychol 6(3):285–305. https://doi.org/10.1207/s1532785xmep0603_3

    Article  Google Scholar 

  254. Soria Morillo LM, Alvarez-Garcia JA, Gonzalez-Abril L, Ortega Ramirez JA (2016) Discrete classification technique applied to TV advertisements liking recognition system based on low-cost EEG headsets. Biomed Eng Online 15(Suppl 1):75–82. https://doi.org/10.1186/s12938-016-0181-2

    Article  Google Scholar 

  255. Soria Morillo LM, Alvarez Garcia JA, Gonzalez-Abril L, Ortega Ramirez JA (2015) Advertising liking recognition technique applied to neuromarketing by using low-cost EEG headset. Bioinform Biomed Eng 9044:701–709. https://doi.org/10.1007/978-3-319-16480-9_68

    Article  Google Scholar 

  256. Steriade M (1999) Coherent oscillations and short-term plasticity in corticothalamic networks. Trends Neurosci 22(8):337–345. https://doi.org/10.1016/S0166-2236(99)01407-1

    Article  Google Scholar 

  257. Stewart DW, Furse DH (1982) Applying psychophysiological measures to marketing and advertising research problems. Curr Issues Res Advert 5(1):1–38. https://doi.org/10.1080/01633392.1982.10505319

    Article  Google Scholar 

  258. Telpaz A, Webb R, Levy DJ (2015) Using EEG to Predict Consumers’ Future Choices. J Market Res 52(4):511–529. https://doi.org/10.1509/jmr.13.0564

    Article  Google Scholar 

  259. Thomas A, Hammer A, Beibst G, Muente TF (2013) An ERP-study of brand and no-name products. Bmc Neurosci 14:149. https://doi.org/10.1186/1471-2202-14-149

    Article  Google Scholar 

  260. Touchette B, Lee S (2017) Measuring neural responses to apparel product attractiveness: an application of frontal asymmetry theory. Cloth Text Res J 35(1):3–15. https://doi.org/10.1177/0887302X16673157

    Article  Google Scholar 

  261. Treleaven-Hassard S, Gold J, Bellman S, Schweda A, Ciorciari J, Critchley C, Varan D (2010) Using the P3a to gauge automatic attention to interactive television advertising. J Econ Psychol 31(5):777–784. https://doi.org/10.1016/j.joep.2010.03.007

    Article  Google Scholar 

  262. Tyson-Carr J, Soto V, Kokmotou K, Roberts H, Fallon N, Byrne A, Giesbrecht T, Stancak A (2020) Neural underpinnings of value-guided choice during auction tasks: an eye-fixation related potentials study. Neuroimage. https://doi.org/10.1016/j.neuroimage.2019.116213

    Article  Google Scholar 

  263. Ullah A, Baloch G, Ahmed A, Buriro AB, Junaid A, Ahmed B, Akhtar S (2022) Neuromarketing solutions based on EEG signal analysis using machine learning. Internat J Adv Comp Sci Appl. https://doi.org/10.14569/IJACSA.2022.0130137

    Article  Google Scholar 

  264. Uva T, Freitas Paiva CLT (2015) Neuroscience technologies in marketing: a study of gender and TV advertisements using electroencephalography. Internat J Technol Market 10(4):362–380. https://doi.org/10.1504/IJTMKT.2015.072181

    Article  Google Scholar 

  265. Varan D, Lang A, Barwise P, Weber R, Bellman S (2015) How reliable are neuromarketers’ measures of advertising effectiveness? Data from ongoing research holds no common truth among vendors. J Advert Res 55(2):176–191. https://doi.org/10.2501/JAR-55-2-176-191

    Article  Google Scholar 

  266. Vecchiato G, Fallani FV, Astolfi L, Toppi J, Cincotti F, Mattia D, Salinari S, Babiloni F (2010) The issue of multiple univariate comparisons in the context of neuroelectric brain mapping: an application in a neuromarketing experiment. J Neurosci Methods 191(2):283–289. https://doi.org/10.1016/j.jneumeth.2010.07.009

    Article  Google Scholar 

  267. Vecchiato G, Di Flumeri G, Maglione AG, Cherubino P, Kong W, Trettel A, Babiloni F (2014) An electroencephalographic peak density function to detect memorization during the observation of TV commercials. In: Vecchiato G (ed) Annual international conference of the IEEE engineering in medicine and biology society. IEEE, Chicago, pp 6969–6972. https://doi.org/10.1109/EMBC.2014.6945231

    Chapter  Google Scholar 

  268. Vecchiato G, Kong W, Maglione AG, Wei D (2012) Understanding the impact of TV commercials: electrical neuroimaging. IEEE Pulse 3(3):42–47. https://doi.org/10.1109/MPUL.2012.2189171

    Article  Google Scholar 

  269. Vecchiato G, Maglione AG, Cherubino P, Wasikowska B, Wawrzyniak A, Latuszynska A, Latuszynska M, Nermend K, Graziani I, Leucci MR, Trettel A, Babiloni F (2014) Neurophysiological tools to investigate consumer’s gender differences during the observation of TV commercials. Comput Math Methods Med 2014:912981. https://doi.org/10.1155/2014/912981

    Article  Google Scholar 

  270. Vecchiato G, Astolfi L, Cincotti F, Fallani FDV, Sorrentino DM, Mattia D, Salinari S, Bianchi L, Toppi J, Aloise F (2010) Patterns of cortical activity during the observation of public service announcements and commercial advertisings. Nonlinear Biomed Phys 4(1):1–9

    Google Scholar 

  271. Vecchiato G, Astolfi L, De Vico Fallani F, Cincotti F, Mattia D, Salinari S, Soranzo R, Babiloni F (2010) Changes in brain activity during the observation of TV commercials by using EEG, GSR and HR measurements. Brain Topogr 23(2):165–179. https://doi.org/10.1007/s10548-009-0127-0

    Article  Google Scholar 

  272. Vecchiato G, Astolfi L, Tabarrini A, Salinari S, Mattia D, Cincotti F, Bianchi L, Sorrentino D, Aloise F, Soranzo R (2010) EEG analysis of the brain activity during the observation of commercial, political, or public service announcements. Comput Intel Neurosci. https://doi.org/10.1155/2010/985867

    Article  Google Scholar 

  273. Vecchiato G, Cherubino P, Maglione AG, Ezquierro MTH, Marinozzi F, Bini F, Trettel A, Babiloni F (2014) How to measure cerebral correlates of emotions in marketing relevant tasks. Cog Comput 6(4):856–871. https://doi.org/10.1007/s12559-014-9304-x

    Article  Google Scholar 

  274. Vecchiato G, Kong W, Maglione AG, Cherubino P, Trettel A, Babiloni F (2014) Cross-cultural analysis of neuroelectrical cognitive and emotional variables during the appreciation of TV commercials. Neuropsychol Trends 16(16):23–29

    Article  Google Scholar 

  275. Vecchiato G, Toppi J, Astolfi L, De Vico Fallani F, Cincotti F, Mattia D, Bez F, Babiloni F (2011) Spectral EEG frontal asymmetries correlate with the experienced pleasantness of TV commercial advertisements. Med Biol Eng Comp 49(5):579–583. https://doi.org/10.1007/s11517-011-0747-x

    Article  Google Scholar 

  276. Wagner J, Makeig S, Gola M, Neuper C, Müller-Putz G (2016) Distinct β band oscillatory networks subserving motor and cognitive control during gait adaptation. J Neurosci 36(7):2212–2226. https://doi.org/10.1523/JNEUROSCI.3543-15.2016

    Article  Google Scholar 

  277. Wajid A, Raziq MM, Ahmed QM, Ahmad M (2021) Observing viewers’ self-reported and neurophysiological responses to message appeal in social media advertisements. J Retail Cons Serv 59:102373. https://doi.org/10.1016/j.jretconser.2020.102373

    Article  Google Scholar 

  278. Walsh AM, Duncan SE, Bell MA, O’Keefe SF, Gallagher DL (2017) Integrating implicit and explicit emotional assessment of food quality and safety concerns. Food Qual Prefer 56:212–224. https://doi.org/10.1016/j.foodqual.2016.11.002

    Article  Google Scholar 

  279. Wang C, Li Y, Luo X, Ma Q, Fu W, Fu H (2018) The effects of money on fake rating behavior in E-commerce: electrophysiological time course evidence from consumers. Front Neurosci 12:156. https://doi.org/10.3389/fnins.2018.00156

    Article  Google Scholar 

  280. Wang D, Zhou C, Chang Y (2015) Acute exercise ameliorates craving and inhibitory deficits in methamphetamine: an ERP study. Physiol Behav 147:38–46. https://doi.org/10.1016/j.physbeh.2015.04.008

    Article  Google Scholar 

  281. Wang J, Han W (2014) The impact of perceived quality on online buying decisions: an event-related potentials perspective. NeuroReport 25(14):1091–1098. https://doi.org/10.1097/WNR.0000000000000233

    Article  Google Scholar 

  282. Wang Q, Wedel M, Huang L, Liu X (2018) Effects of model eye gaze direction on consumer visual processing: evidence from China and America. Inform Manage 55(5):588–597. https://doi.org/10.1016/j.im.2017.12.003

    Article  Google Scholar 

  283. Wang RWY, Chang Y, Chuang S (2016) EEG spectral dynamics of video commercials: impact of the narrative on the branding product preference. Sci Rep 6:36487. https://doi.org/10.1038/srep36487

    Article  Google Scholar 

  284. Wang RW, Chen YC, Liu I, Chuang SW (2018) Temporal and spectral EEG dynamics can be indicators of stealth placement. Sci Rep 8:1–17. https://doi.org/10.1038/s41598-018-27294-4

    Article  Google Scholar 

  285. Wang TC, Tsai CL, Tang TW (2018) Exploring advertising effectiveness of tourist hotels’ marketing images containing nature and performing arts: an eye-tracking analysis. Sustainability 10(9):3038. https://doi.org/10.3390/su10093038

    Article  Google Scholar 

  286. Wang X, Ma Q, Wang C (2012) N400 as an index of uncontrolled categorization processing in brand extension. Neurosci Let 525(1):76–81. https://doi.org/10.1016/j.neulet.2012.07.043

    Article  Google Scholar 

  287. Wang Y, Ma N, Wang J, Hu Z, Liu Z, He J (2020) Prediction of product design decision making: an investigation of eye movements and EEG features. Adv Eng Inform. https://doi.org/10.1016/j.aei.2020.101095

    Article  Google Scholar 

  288. Wang YJ, Minor MS (2008) Validity, reliability, and applicability of psychophysiological techniques in marketing research. Psychol Market 25(2):197–232. https://doi.org/10.1002/mar.20206

    Article  Google Scholar 

  289. Wang Y, Hsieh C (2018) Explore technology innovation and intelligence for IoT (Internet of Things) based eyewear technology. Technol Forecast Soc Change 127:281–290. https://doi.org/10.1016/j.techfore.2017.10.001

    Article  Google Scholar 

  290. Wedel M, Pieters R (2017) A review of eye-tracking research in marketing. In: Naresh KM (ed) Review of marketing research. Routlege, London, pp 123–147

    Chapter  Google Scholar 

  291. Wei Z, Wu C, Wang X, Supratak A, Wang P, Guo Y (2018) Using support vector machine on EEG for advertisement impact assessment. Front Neurosci 12:76. https://doi.org/10.3389/fnins.2018.00076

    Article  Google Scholar 

  292. White KR, Crites SL Jr, Taylor JH, Corral G (2009) Wait, what? Assessing stereotype incongruities using the N400 ERP component. Soc Cognit Affect Neurosci 4(2):191–198. https://doi.org/10.1093/scan/nsp004

    Article  Google Scholar 

  293. Wriessnegger SC, Hackhofer D, Müller-Putz GR (2015) Classification of unconscious like/dislike decisions: first results towards a novel application for BCI technology. In: 2015 37th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), 2331–2334

  294. Yadava M, Kumar P, Saini R, Roy PP, Dogra DP (2017) Analysis of EEG signals and its application to neuromarketing. Multimed Tools Appl 76(18):19087–19111. https://doi.org/10.1007/s11042-017-4580-6

    Article  Google Scholar 

  295. Yang D (2018) Exploratory neural reactions to framed advertisement messages of smoking cessation. Soc Market Quarter 24(3):216–232. https://doi.org/10.1177/1524500418788306

    Article  Google Scholar 

  296. Yang S (2015) An eye-tracking study of the elaboration likelihood model in online shopping. Electron Comm Res Appl 14(4):233–240. https://doi.org/10.1016/j.elerap.2014.11.007

    Article  Google Scholar 

  297. Yang T, Lee S, Seomoon E, Kim SP (2018) Characteristics of human brain activity during the evaluation of service-to-service brand extension. Front Hum Neurosci 12:44. https://doi.org/10.3389/fnhum.2018.00044

    Article  Google Scholar 

  298. Yang T, Do-Young L, Kwak Y, Choi J, Kim C, Sung-Phil K (2015) Evaluation of TV commercials using neurophysiological responses. J Physiol Anthropol. https://doi.org/10.1186/s40101-015-0056-4

    Article  Google Scholar 

  299. Yang T, Kim S (2019) Group-level neural responses to service-to-service brand extension. Front Neurosci 13:676. https://doi.org/10.3389/fnins.2019.00676

    Article  Google Scholar 

  300. Yazid AF, Mohd SM, Khan AR, Ali KR, Kamarudin S, Jan NM (2020) Decision-making analysis using arduino-based electroencephalography (EEG): an exploratory study for marketing strategy. Internat J Adv Comput Sci Appl 11(9):236–243. https://doi.org/10.14569/IJACSA.2020.0110927

    Article  Google Scholar 

  301. Yen C, Chiang M (2021) Examining the effect of online advertisement cues on human responses using eye-tracking, EEG, and MRI. Behav Brain Res 402:113128. https://doi.org/10.1016/j.bbr.2021.113128

    Article  Google Scholar 

  302. Ying X (2019) An overview of overfitting and its solutions. J Phys 1168(2):022022. https://doi.org/10.1088/1742-6596/1168/2/022022

    Article  Google Scholar 

  303. Yılmaz B, Korkmaz S, Arslan DB, Güngör E, Asyalı MH (2014) Like/dislike analysis using EEG: determination of most discriminative channels and frequencies. Comput Method Prog Biomed 113(2):705–713. https://doi.org/10.1016/j.cmpb.2013.11.010

    Article  Google Scholar 

  304. Yoon C, Gonzalez R, Bechara A, Berns GS, Dagher AA, Dubé L, Huettel SA, Kable JW, Liberzon I, Plassmann H, Smidts A, Spence C (2012) Decision neuroscience and consumer decision making. Market Let 23(2):473–485. https://doi.org/10.1007/s11002-012-9188-z

    Article  Google Scholar 

  305. Young C (2002) Brain waves, picture sorts (R), and branding moments. J Advert Res 42(4):42–53. https://doi.org/10.2501/JAR-42-4-42-53

    Article  Google Scholar 

  306. Yu W, Sun Z, Xu T, Ma Q (2018) Things become appealing when i win: neural evidence of the influence of competition outcomes on brand preference. Front Neurosci 12:779. https://doi.org/10.3389/fnins.2018.00779

    Article  Google Scholar 

  307. Zamani J, Naieni AB (2020) Best feature extraction and classification algorithms for EEG signals in neuromarketing. Front Biomed Technol 7(3):186–191. https://doi.org/10.18502/fbt.v7i3.4621

    Article  Google Scholar 

  308. Zeng L, Lin M, Xiao K, Wang J, Zhou H (2021) Prediction of consumer preference for sports shoes with EEG: an application of neuromarketing. Front Hum Neurosci. p 775.

  309. Zhang J, Tang C, Guo L, Xu H (2018) A longitudinal investigation of customer cooperation in services: The role of appraisal of cooperation behaviors. Psychol Market 35(12):957–967. https://doi.org/10.1002/mar.21148

    Article  Google Scholar 

  310. Zhang W, Jin J, Wang A, Ma Q, Yu H (2019) Consumers’ implicit motivation of purchasing luxury brands: an EEG study. Psychol Res Behav Manage 12:913–929. https://doi.org/10.2147/PRBM.S215751

    Article  Google Scholar 

  311. Zhang X (2020) The influences of brand awareness on consumers’ cognitive process: an event-related potentials study. Front Neurosci. https://doi.org/10.3389/fnins.2020.00549

    Article  Google Scholar 

  312. Zhao M, Wang J, Zhang H, Zhao G (2019) ERP perspective analysis of PSS component and decision-making. Sustainability. https://doi.org/10.3390/su11041063

    Article  Google Scholar 

  313. Zheng W, Liu W, Lu Y, Lu B, Cichocki A (2018) Emotionmeter: a multimodal framework for recognizing human emotions. IEEE Trans Cybern 49(3):1110–1122. https://doi.org/10.1109/TCYB.2018.2797176

    Article  Google Scholar 

  314. Zhu S, Qi J, Hu J, Hao S (2022) A new approach for product evaluation based on integration of EEG and eye-tracking. Adv Eng Inform 52:101601. https://doi.org/10.1016/j.aei.2022.101601

    Article  Google Scholar 

  315. Zubair M, Iqbal S, Usman SM, Awais M, Wang R, Wang X (2020) Message framing and self-conscious emotions help to understand pro-environment consumer purchase intention: an ERP study. Sci Rep. https://doi.org/10.1038/s41598-020-75343-8

    Article  Google Scholar 

Download references

Acknowledgements

Not applicable.

Funding

This work was funded by the Knowledge Transfer Partnership (KTP) scheme, [KTP Prog. No.: KTP 12421; KB ref. no.: R3200 C659], Innovate UK, UKRI, Keele University, Staffordshire, UK, and risual Limited, Staffordshire, UK.

Author information

Authors and Affiliations

  1. Keele Business School, Denise Coates Foundation Building, Keele University, Staffordshire, ST5 5AA, UK

    Adam Byrne & Colin Rigby

  2. Faculty of Humanities and Social Sciences, Keele University, Staffordshire, UK

    Adam Byrne & Emma Bonfiglio

  3. Risual Limited, Staffordshire, UK

    Adam Byrne

  4. School of Psychology, Keele University, Staffordshire, UK

    Nicky Edelstyn

Authors
  1. Adam Byrne
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Emma Bonfiglio
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Colin Rigby
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Nicky Edelstyn
    View author publications

    You can also search for this author in PubMed Google Scholar

Contributions

AB was responsible for the primary data collection and the writing of the manuscript. EB administrated funding and reviewed the final manuscript. CR supervised the systematic review and reviewed the final manuscript. NE supervised the systematic review, aided in the writing of the manuscript and reviewed the final manuscript. All authors read and approved the final manuscript.

Corresponding author

Correspondence to Adam Byrne.

Ethics declarations

Competing interests

The authors declare that they have no competing interests.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Byrne, A., Bonfiglio, E., Rigby, C. et al. A systematic review of the prediction of consumer preference using EEG measures and machine-learning in neuromarketing research. Brain Inf. 9, 27 (2022). https://doi.org/10.1186/s40708-022-00175-3

Download citation

  • Received:

  • Accepted:

  • Published:

  • DOI: https://doi.org/10.1186/s40708-022-00175-3

Keywords