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The Regulation (EU) 2016/679 on the protection of natural persons regarding the processing of personal data (GDPR) is one of the key fundamental pieces of European legislation to protect human rights and freedoms. However, the development of AI systems that are capable of collecting and processing large amounts of data and predicting user habits and emotional states has affected traditional legal categories and tested their resilience. This paper assesses the limits of the current formulation of the GDPR which does not take expressly into account the category of inferred data as a special category of data. Furthermore, it questions whether the toolbox put in place by the GDPR is still effective in protecting data subjects from practices such as neuromarketing and eye-tracking systems. It shows that it is certainly the essential starting point, but that, on the other hand, cannot be spared criticism. For this, in the recent years, the European legislator has adopted further legislations including, in particular, the Digital Services Act (DSA) and the Artificial Intelligence Act (AIA). Although representing a step forward in protection against such technologies, they each have critical aspects that need to be considered. [ABSTRACT FROM AUTHOR]

Organizations that embrace innovative business strategies position themselves well for success. In this evolving technological landscape, businesses are increasingly focused on understanding consumer behaviors and preferences to tailor their products and services effectively. Notably, integrating neuroscience has become pivotal in achieving these goals, requiring its incorporation throughout various business processes. This comprehensive research review conducts a meta-analysis, synthesizing findings from numerous studies on applying neuroscience across diverse business domains. The primary objective is to identify innovative neuroscience approaches that can significantly revolutionize businesses. Through a meticulous literature exploration using PRISMA analysis, we discerned broad themes and further refined them into critical sub-themes. This thematic categorization enables a focused examination of each area of business research. The findings in this study uncovered vital intersections between neuroscience and business, offering valuable insights for corporations and researchers. In essence, the integration of neuroscientific techniques and artificial intelligence (AI)-based softwares into business practices is no longer optional, but a necessity for businesses to stay competitive in today's fast-paced world. This study, therefore, plays a crucial role in connecting the state-of-the-art tools and techniques in neuroscience and traditional business practices. It paves the way for a new era of business operations, where science and technology work hand in hand with commerce to create a more prosperous and sustainable future. In conclusion, the paper emphasizes the importance of incorporating neuroscience techniques and newly developed AI-based softwares into business practices. This integration can yield precise and favorable outcomes, offering businesses the tools to thrive in an ever-changing market environment. [ABSTRACT FROM AUTHOR]

Neuromarketing is an emerging research field that aims to understand consumers' decision-making processes when choosing which product to buy. This information is highly sought after by businesses looking to improve their marketing strategies by understanding what leaves a positive or negative impression on consumers. It has the potential to revolutionize the marketing industry by enabling companies to offer engaging experiences, create more effective advertisements, avoid the wrong marketing strategies, and ultimately save millions of dollars for businesses. Therefore, good documentation is necessary to capture the current research situation in this vital sector. In this article, we present a systematic review of EEG-based Neuromarketing. We aim to shed light on the research trends, technical scopes, and potential opportunities in this field. We reviewed recent publications from valid databases and divided the popular research topics in Neuromarketing into five clusters to present the current research trend in this field. We also discuss the brain regions that are activated when making purchase decisions and their relevance to Neuromarketing applications. The article provides appropriate illustrations of marketing stimuli that can elicit authentic impressions from consumers' minds, the techniques used to process and analyze recorded brain data, and the current strategies employed to interpret the data. Finally, we offer recommendations to upcoming researchers to help them investigate the possibilities in this area more efficiently in the future. [ABSTRACT FROM AUTHOR]

Non-Profit Organizations (NPOs) find themselves in a very competitive environment, as everyday consumers are constantly exposed to numerous advertisements; thus, they must find ways to capture consumers' attention. The objective of this study is to explore how the different elements (image, text, logo) of print advertisements of NPOs using different emotional appeals (positive and negative) of a familiar and unfamiliar brand influence donation behaviour and the attitude toward the ad (Aad). Using eye-tracking technology and a survey, we conducted two experiments, one with unfamiliar brands of NPOs and another with a familiar brand. The results showed the advertisement areas on which participants fixated and their relationship with participants' attitude towards the advertisement and donation behaviour. For unfamiliar NPOs, the less time it took the participant to first fixate on the logo area, the more positive attitude toward the ad when the advertisement used a negative frame. Also, participants spent more time in the image area of negatively framed ads when they had a more positive attitude toward the ad. On the other hand, for a familiar brand, the time to first fixate on the logo area had a negative correlation with the donation behaviour, indicating that the less time it takes to first fixate on the logo area, the more participants chose to donate. [ABSTRACT FROM AUTHOR]

Neuromarketing relies on brain-computer interface technology to understand consumer preferences for products and services. Marketers spend approximately 400 billion dollars each year on advertising and promotion in traditional marketing. Traditional marketing approaches cannot fully explain or capture consumers' real-time decision-making. On the other hand, neuromarketing promises to get around these limitations. In this study, we presented a multi-channel electroencephalography (EEG)-based deep learning approach to classify consumers' preferences. The EEG signals were recorded from 25 subjects using 14 channels. The channels were categorized according to the frontal, parietal, temporal, and occipital brain regions. The multitaper spectral analysis approach was then used to extract the feature vectors. Using the extracted feature vectors, the performances of bidirectional long-short-term memory (Bidirectional-LSTM) deep learning, support vector machine (SVM), and k-nearest neighbors (k-NN) machine learning algorithms were compared. The performance of the algorithms was analyzed using frontal, central, parietal, temporal, and occipital brain regions and all channels. Bidirectional-LSTM deep learning algorithm attained the highest accuracy among the other experiments. According to the placement of the channels in the brain regions, the highest accuracy value was 96.83% using Bidirectional-LSTM deep learning algorithm and this was achieved by using electrodes in the frontal region. The performance results analysis was found to be 0.99 recall, 0.95 precision, 0.94 specificity, and 0.97 f1-measure. As a result, this study offers proof of deep learning algorithms' effectiveness in neuromarketing applications. [ABSTRACT FROM AUTHOR]

This study aims to select the physiological and neurophysiological studies utilized in advertising and to address the fragmented comprehension of consumers' mental responses to advertising held by marketers and advertisers. To fill the gap, the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) framework was employed to select relevant articles, and bibliometric analysis was conducted to determine global trends and advancements in advertising and neuromarketing. The study selected and analyzed forty-one papers from the Web of Science (WoS) database from 2009–2020. The results indicated that Spain, particularly the Complutense University of Madrid, was the most productive country and institution, respectively, with 11 and 3 articles. The journal Frontiers in Psychology was the most prolific, with eight articles. The article "Neuromarketing: The New Science of Consumer Behavior" had the most citations (152 T.Cs). Additionally, the researchers discovered that the inferior frontal and middle temporal gyri were associated with pleasant and unpleasant emotions, respectively, while the right superior temporal and right middle frontal gyrus was connected to high and low arousal. Furthermore, the right prefrontal cortex (PFC) and left PFC were linked to withdrawal and approach behaviors. In terms of the reward system, the ventral striatum played a critical role, while the orbitofrontal cortex and ventromedial PFC were connected to perception. As far as we know, this is the first paper that focused on the global academic trends and developments of neurophysiological and physiological instruments used in advertising in the new millennium, emphasizing the significance of intrinsic and extrinsic emotional processes, endogenous and exogenous attentional processes, memory, reward, motivational attitude, and perception in advertising campaigns. [ABSTRACT FROM AUTHOR]

Nowadays, advertising is regarded as one of the vital elements of marketing tools' promotional strategies. Therefore, advertising is very important to businesses' marketing strategies and policies. In light of this, the purpose of the current study was to ascertain the influence of endorsers' gender and the level of attention given to various aspects of advertising. A quasi-experimental study was used for the current investigation. All students at Mashhad's Ferdowsi University made up the study's statistical population. 80 students were chosen as the research sample out of the entire student body. Eye motions were captured using an eye tracking gadget. According to research findings, the number and length of fixes on advertisement items were significantly influenced by the popularity of their endorsers. However, there was no statistically significant difference between the genders in terms of the quantity and length of fixations. These conclusions suggest that the popularity of the endorser is a key factor in commercials, but that the endorser's gender has little effect. [ABSTRACT FROM AUTHOR]

Neuromarketing is a continuously evolving field that utilises neuroimaging technologies to explore consumers' behavioural responses to specific marketing-related stimulation, and furthermore introduces novel marketing tools that could complement the traditional ones like questionnaires. In this context, the present paper introduces a multimodal Neuromarketing dataset that encompasses the data from 42 individuals who participated in an advertising brochure-browsing scenario. In more detail, participants were exposed to a series of supermarket brochures (containing various products) and instructed to select the products they intended to buy. The data collected for each individual executing this protocol included: (i) encephalographic (EEG) recordings, (ii) eye tracking (ET) recordings, (iii) questionnaire responses (demographic, profiling and product related questions), and (iv) computer mouse data. NeuMa dataset has both dynamic and multimodal nature and, due to the narrow availability of open relevant datasets, provides new and unique opportunities for researchers in the field to attempt a more holistic approach to neuromarketing. [ABSTRACT FROM AUTHOR]

Brain–computer interface (BCI), an emerging technology that facilitates communication between brain and computer, has attracted a great deal of research in recent years. Researchers provide experimental results demonstrating that BCI can restore the capabilities of physically challenged people, hence improving the quality of their lives. BCI has revolutionized and positively impacted several industries, including entertainment and gaming, automation and control, education, neuromarketing, and neuroergonomics. Notwithstanding its broad range of applications, the global trend of BCI remains lightly discussed in the literature. Understanding the trend may inform researchers and practitioners on the direction of the field, and on where they should invest their efforts more. Noting this significance, we have analyzed 25,336 metadata of BCI publications from Scopus to determine advancement of the field. The analysis shows an exponential growth of BCI publications in China from 2019 onwards, exceeding those from the United States that started to decline during the same period. Implications and reasons for this trend are discussed. Furthermore, we have extensively discussed challenges and threats limiting exploitation of BCI capabilities. A typical BCI architecture is hypothesized to address two prominent BCI threats, privacy and security, as an attempt to make the technology commercially viable to the society. [ABSTRACT FROM AUTHOR]

Preserving customers' expectations and understanding factors affecting their purchasing decisions are crucial in designing effective marketing and advertising strategies. However, constantly and swiftly changing the customers' interests and consumption behaviors make it inevitable to utilize sophisticated tools and approaches based on advanced technologies. Among them, by measuring the customers' physiological and neural signals, studying the customers' cognitive and affective responses to marketing stimuli, neuromarketing provides deep insight into the customers' motivations, preferences, and decisions. Recently, the internet of everything (IoE) has brought many new opportunities to the industry and has attracted the attention of many researchers in recent years. The main objective of this paper is to address how the IoE would empower neuromarketing techniques. Hence, an in-depth understanding of current research issues as well as emerging trends would help meet this goal. In this paper, a comprehensive review has been done by reviewing numerous journal and conference papers from various academic databases (sciencedirect.com, IEEE Xplore, Springer, Elsevier, Wiley, ACM digital library) based on applying various filtrations on specific keywords to identify and categorize the IoE devices in the neuromarketing field. In particular, we discussed the importance and the applications of IoE gadgets and devices, especially wearable medical technologies in eight neuromarketing techniques. Finally, we described critical existing challenges and limitations in neuromarketing and IoE gadgets that researchers have dealt with in this field of research. [ABSTRACT FROM AUTHOR]

Electroencephalography (EEG)-based emotion recognition is an important technology for human–computer interactions. In the field of neuromarketing, emotion recognition based on group EEG can be used to analyze the emotional states of multiple users. Previous emotion recognition experiments have been based on individual EEGs; therefore, it is difficult to use them for estimating the emotional states of multiple users. The purpose of this study is to find a data processing method that can improve the efficiency of emotion recognition. In this study, the DEAP dataset was used, which comprises EEG signals of 32 participants that were recorded as they watched 40 videos with different emotional themes. This study compared emotion recognition accuracy based on individual and group EEGs using the proposed convolutional neural network model. Based on this study, we can see that the differences of phase locking value (PLV) exist in different EEG frequency bands when subjects are in different emotional states. The results showed that an emotion recognition accuracy of up to 85% can be obtained for group EEG data by using the proposed model. It means that using group EEG data can effectively improve the efficiency of emotion recognition. Moreover, the significant emotion recognition accuracy for multiple users achieved in this study can contribute to research on handling group human emotional states. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

Neuromarketing exploits neuroimaging techniques so as to reinforce the predictive power of conventional marketing tools, like questionnaires and focus groups. Electroencephalography (EEG) is the most commonly encountered neuroimaging technique due to its non-invasiveness, low-cost, and its very recent embedding in wearable devices. The transcription of brainwave patterns to consumer attitude is supported by various signal descriptors, while the quest for profitable novel ways is still an open research question. Here, we suggest the use of sample covariance matrices as alternative descriptors, that encapsulate the coordinated neural activity from distinct brain areas, and the adoption of Riemannian geometry for their handling. We first establish the suitability of Riemannian approach for neuromarketing-related problems and then suggest a relevant decoding scheme for predicting consumers’ choices (e.g., willing to buy or not a specific product). Since the decision-making process involves the concurrent interaction of various cognitive processes and consequently of distinct brain rhythms, the proposed decoder takes the form of an ensemble classifier that builds upon a multi-view perspective, with each view dedicated to a specific frequency band. Adopting a standard machine learning procedure, and using a set of trials (training data) in conjunction with the associated behavior labels (“buy”/ “not buy”), we train a battery of classifiers accordingly. Each classifier is designed to operate in the space recovered from the inter-trial distances of SCMs and to cast a rhythm-depended decision that is eventually combined with the predictions of the rest ones. The demonstration and evaluation of the proposed approach are performed in 2 neuromarketing-related datasets of different nature. The first is employed to showcase the potential of the suggested descriptor, while the second to showcase the decoder’s superiority against popular alternatives in the field. [ABSTRACT FROM AUTHOR]

Advertising for non-profit organizations through television commercials is a valuable means of communication to raise awareness and receive donations. When it comes to social aspects, personal attitudes such as empathy are significant for reinforcing the intention to donate; and the study of eliciting emotions has critical attention in the literature, especially some types of emotion, such as guilt which mediates empathy. Different methodologies have been used to measure consumer emotions when faced with TV ads stimuli: mainly traditional techniques such as interviews or questionnaires after the ads viewing. In the last ten years, there has also been a great interest in new neuroscience techniques applied to measure emotional and cognitive reactions by physiological signals, frame by frame. Our research has applied neuromarketing technologies during the observation of a UNHCR commercial promoting legacy calls. The objective was to study cognitive and emotional reactions in order to increase the effectiveness whilst having the possibility to verify the results by measuring the benefits in terms of calls from contributors. The purpose of this research is to empirically prove the impact in calls thanks to changes in the message framing strategy in non-profit advertising suggested and measured by neuromarketing techniques. Particularly we measured the cerebral activity through an electroencephalogram to obtain an Approach-Withdrawal Index (AW); the heart rate and galvanic skin response through different sensors in the palm of one hand, to obtain an Emotional Index (EI), and finally, eye fixations through an eye tracker device to obtain the visual attention on key visual areas of the ads. After these indicators' recordings on a sample of subjects, some suggestions to modify the advertising were made to create a more effective campaign. The results compared, those elicited by the first version of the spot (LVE) and those by the second version (HVE), confirmed that (1) the number of sellable and legacy calls increased with the message framing strategy modified in the second spot (HVE), (2) a lower cognitive and emotional reactions have been obtained in the final section of HVE, (3) the visual attention on the key information of the phone number to call, in the final call to action frames(CTA), was higher in HVE than in the first version of the spot (LVE), (4) the cognitive approach increased during the same CTA frames in HVE. [ABSTRACT FROM AUTHOR]

Since its emergence, neuromarketing has generated multiple debates about its uses and misuses, the ethics of this highly sensitive data technique, and the lack of regulation for consumer protection. To explore how consumers perceive neuromarketing, a content analysis of user-generated content (UGC) on blogs and social media was conducted. The main purpose of this research was to identify consumers' opinions about neuromarketing, its perceived benefits and risks, and the ethical concerns and implications for those involved in the use of this method. Taking a social media mining approach, the study uncovers the most common neuromarketing concerns discussed by consumers, describes the main participants (or actors) of the neuromarketing narrative and their interrelationships, and evaluates the overall consumer sentiment of neuromarketing. Six main themes and eight actors emerged from the exploratory analysis. Furthermore, the sentiment analysis demonstrated a positive perception of neuromarketing among consumers, but it also revealed some concerns about the lack of regulation and consumer privacy. The study concludes with a discussion of implications for future research. [ABSTRACT FROM AUTHOR]

Rapid growth in structural and functional brain research has led to increasing ethical discussion of what to do about incidental findings within the brains of healthy neuroimaging research participants that have potential health importance, but which are beyond the original aims of the study. This dilemma has been widely debated with respect to general neuroimaging research but has attracted little attention in the context of neuromarketing studies. In this paper, I argue that neuromarketing researchers owe participants the same ethical obligations as other neuroimaging researchers. The financial resources available to neuromarketing firms and the social value of neuromarketing studies should command greater attention to the elucidation and management of incidental findings. However, this needs to be balanced against finite resources available within most public health systems. [ABSTRACT FROM AUTHOR]

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

Brain–computer interface (BCI) technology used to monitor conscious-brain electrical activity via electroencephalogram (EEG) signals has facilitated the detection of human preferences. Recently, great progress has been made in the development of novel paradigms and methods for EEG-based preference detection including attempts to apply BCI research findings in various contexts. Advances in BCI technology have increased the scientists' interest in possible practical applications of BCI technology involving a human–machine interaction. A major objective of our research was to provide an overview of recent advances in EEG-based preference detection applications. As a foundation for our research, we reviewed previous studies in EEG-based neuromarketing and classified them according to their practical domains, research directions, and recording modalities. Another major research goal was to investigate how the analysis of EEG signals using classification algorithms can be applied to recognize and understand consumers' mental states and preferences patterns. To this end, we built three different classification algorithms: the random forest (RF), support vector machine (SVM), and the k-nearest neighbor (KNN). Our results demonstrate that RF is more accurate than either KNN or SVM. [ABSTRACT FROM AUTHOR]