On the use of EEG or MEG brain imaging tools in neuromarketing research

Neural correlates of the non-optimal price: an MEG/EEG study

Introduction

Setting the right price is crucial for effectively positioning products in the market. Conversely, setting a "non-optimal price"-one that is perceived as much lower or higher than the product's true market value-can negatively influence consumer decisions and business results.

Methods

We conducted two electroencephalography (EEG) studies and one magnetoencephalography (MEG) study to investigate brain mechanisms underlying the perception of prices during a price judgment task. In each trial, participants were exposed to a mobile phone image (iPhone, Nokia, or Xiaomi) followed by a price, and instructed to judge whether the price was high or low based on a target word ("cheap" or "expensive").

Results

In both EEG experiments, we found a strong N400-like response to the incongruent target words following prices that substantially differed from the real market value of the mobile phone. The MEG experiment extended these findings by localizing the brain source of the price-related, M400-like response, the magnetic counterpart to the N400 component, in the ventromedial prefrontal cortex (vmPFC) and anterior cingulate cortex (ACC) implicated in value-based and reward-based learning, respectively. Our results demonstrate that both the brain sources and the timing of the price-related M400 response differed from those of the standard M400 evoked by semantically incongruent words.

Discussion

Overall, our results suggest that the N400-like response can serve as a neural marker of price-product incongruity, with potential applications in consumer research.

Multimodal consumer choice prediction using EEG signals and eye tracking

Marketing plays a vital role in the success of a business, driving customer engagement, brand recognition, and revenue growth. Neuromarketing adds depth to this by employing insights into consumer behavior through brain activity and emotional responses to create more effective marketing strategies. Electroencephalogram (EEG) has typically been utilized by researchers for neuromarketing, whereas Eye Tracking (ET) has remained unexplored. To address this gap, we propose a novel multimodal approach to predict consumer choices by integrating EEG and ET data. Noise from EEG signals is mitigated using a bandpass filter, Artifact Subspace Reconstruction (ASR), and Fast Orthogonal Regression for Classification and Estimation (FORCE). Class imbalance is handled by employing the Synthetic Minority Over-sampling Technique (SMOTE). Handcrafted features, including statistical and wavelet features, and automated features from Convolutional Neural Network and Long Short-Term Memory (CNN-LSTM), have been extracted and concatenated to generate a feature space representation. For ET data, preprocessing involved interpolation, gaze plots, and SMOTE, followed by feature extraction using LeNet-5 and handcrafted features like fixations and saccades. Multimodal feature space representation was generated by performing feature-level fusion for EEG and ET, which was later fed into a meta-learner-based ensemble classifier with three base classifiers, including Random Forest, Extended Gradient Boosting, and Gradient Boosting, and Random Forest as the meta-classifier, to perform classification between buy vs. not buy. The performance of the proposed approach is evaluated using a variety of performance metrics, including accuracy, precision, recall, and F1 score. Our model demonstrated superior performance compared to competitors by achieving 84.01% accuracy in predicting consumer choices and 83% precision in identifying positive consumer preferences.

The effects of music mood and binaural beats on academic advertising

How music affects our mood, cognition, and feelings has been studied extensively. Since the effect of music on mood and cognition has been demonstrated many times, it has found significant applications, particularly in advertising. In recent years, the use of music in advertising has grown significantly, with 86 % of advertisements now incorporating some form of musical stimuli. Our study specifically analyzed the effect of music mood in advertising while introducing the new concept of binaural beats. Conducted in a lab setting, the study incorporated biometric measures such as electroencephalography (EEG) and eye-tracking to enhance the research. The results revealed that calming music combined with binaural beats led to the highest levels of information retention and engagement, as indicated by increased left frontal beta power, relative theta power, and area of interest (AOI) dwell time percentages. Left frontal beta power is associated with increased attention and cognitive engagement, while relative theta power is linked to enhanced memory encoding and relaxation. The area of interest (AOI) dwell time percentages reflects the time participants focused on key areas of the advertisement, indicating higher attention levels. Although the study found unique connections between music mood and binaural beats, calming music with binaural beats produced the most favorable conditions for attention and memory retention. These findings provide valuable guidelines for future marketing strategies, particularly in academic advertising.

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

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

Unraveling neural pathways of political engagement: bridging neuromarketing and political science for understanding voter behavior and political leader perception

Introduction

Political neuromarketing is an emerging interdisciplinary field integrating marketing, neuroscience, and psychology to decipher voter behavior and political leader perception. This interdisciplinary field offers novel techniques to understand complex phenomena such as voter engagement, political leadership, and party branding.

Methods

This study aims to understand the neural activation patterns of voters when they are exposed to political leaders using functional near-infrared spectroscopy (fNIRS) and machine learning methods. We recruited participants and recorded their brain activity using fNIRS when they were exposed to images of different political leaders.

Results

This neuroimaging method (fNIRS) reveals brain regions central to brand perception, including the dorsolateral prefrontal cortex (dlPFC), the dorsomedial prefrontal cortex (dmPFC), and the ventromedial prefrontal cortex (vmPFC). Machine learning methods were used to predict the participants' perceptions of leaders based on their brain activity. The study has identified the brain regions that are involved in processing political stimuli and making judgments about political leaders. Within this study, the best-performing machine learning model, LightGBM, achieved a highest accuracy score of 0.78, underscoring its efficacy in predicting voters' perceptions of political leaders based on the brain activity of the former.

Discussion

The findings from this study provide new insights into the neural basis of political decision-making and the development of effective political marketing campaigns while bridging neuromarketing, political science, and machine learning, in turn enabling predictive insights into voter preferences and behavior.

Exploring global trends and future directions in advertising research: A focus on consumer behavior

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.

Do you like it or not? Identifying preference using an electroencephalogram during the viewing of short videos

Accurately predicting whether a short video will be liked by viewers is a topic of interest to media researchers. This study used an electroencephalogram (EEG) to record neural activity in 109 participants as they watched short videos (16 clips per person) to see which neural signals reflected viewers' preferences. The results showed that, compared with the short videos they disliked, individuals would experience positive emotions [indexed by a higher theta power, lower (beta - theta)/(beta + theta) score], more relaxed states (indexed by a lower beta power), lower levels of mental engagement and alertness [indexed by a lower beta/(alpha + theta) score], and devote more attention (indexed by lower alpha/theta) when watching short videos they liked. We further used artificial neural networks to classify the neural signals of different preferences induced by short videos. The classification accuracy was the highest when using data from bands over the whole brain, which was 75.78%. These results may indicate the potential of EEG measurement to evaluate the subjective preferences of individuals for short videos.

Optimized adaptive neuro-fuzzy inference system based on hybrid grey wolf-bat algorithm for schizophrenia recognition from EEG signals

Schizophrenia is a chronic mental disorder that impairs a person's thinking capacity, feelings and emotions, behavioural traits, etc., Emotional distortions, delusions, hallucinations, and incoherent speech are all some of the symptoms of schizophrenia, and cause disruption of routine activities. Computer-assisted diagnosis of schizophrenia is significantly needed to give its patients a higher quality of life. Hence, an improved adaptive neuro-fuzzy inference system based on the Hybrid Grey Wolf-Bat Algorithm for accurate prediction of schizophrenia from multi-channel EEG signals is presented in this study. The EEG signals are pre-processed using a Butterworth band pass filter and wICA initially, from which statistical, time-domain, frequency-domain, and spectral features are extracted. Discriminating features are selected using the ReliefF algorithm and are then forwarded to ANFIS for classification into either schizophrenic or normal. ANFIS is optimized by the Hybrid Grey Wolf-Bat Algorithm (HWBO) for better efficiency. The method is experimented on two separate EEG datasets-1 and 2, demonstrating an accuracy of 99.54% and 99.35%, respectively, with appreciable F1-score and MCC. Further experiments reveal the efficiency of the Hybrid Wolf-Bat algorithm in optimizing the ANFIS parameters when compared with traditional ANFIS model and other proven algorithms like genetic algorithm-ANFIS, particle optimization-ANFIS, crow search optimization algorithm-ANFIS and ant colony optimization algorithm-ANFIS, showing high R2 value and low RSME value. To provide a bias free classification, tenfold cross validation is performed which produced an accuracy of 97.8% and 98.5% on the two datasets respectively. Experimental outcomes demonstrate the superiority of the Hybrid Grey Wolf-Bat Algorithm over the similar techniques in predicting schizophrenia.

Automatic facial coding predicts self-report of emotion, advertisement and brand effects elicited by video commercials

Introduction

Consumers' emotional responses are the prime target for marketing commercials. Facial expressions provide information about a person's emotional state and technological advances have enabled machines to automatically decode them.

Method

With automatic facial coding we investigated the relationships between facial movements (i.e., action unit activity) and self-report of commercials advertisement emotion, advertisement and brand effects. Therefore, we recorded and analyzed the facial responses of 219 participants while they watched a broad array of video commercials.

Results

Facial expressions significantly predicted self-report of emotion as well as advertisement and brand effects. Interestingly, facial expressions had incremental value beyond self-report of emotion in the prediction of advertisement and brand effects. Hence, automatic facial coding appears to be useful as a non-verbal quantification of advertisement effects beyond self-report.

Discussion

This is the first study to measure a broad spectrum of automatically scored facial responses to video commercials. Automatic facial coding is a promising non-invasive and non-verbal method to measure emotional responses in marketing.

Temporal and electroencephalography dynamics of surreal marketing

Event-related spectral perturbation analysis was employed in this study to explore whether surreal image designs containing metaphors could influence product marketing effects, including consumers' product curiosity, product comprehension, product preference, and purchase intention. A total of 30 healthy participants aged 21-30 years were recruited. Neurophysiological findings revealed that lower gamma, beta, and theta spectral powers were evoked in the right insula (Brodmann Area 13) by surreal marketing images. This was associated, behaviorally, with the manifestation of higher product curiosity and purchase intention. Based on previous research, the brain functions of this area include novelty, puzzle-solving, and cravings for reward caused by cognitive overload.

Mapping and understanding of correlated electroencephalogram (EEG) responses to the newsvendor problem

Decision-making is one of the most critical activities of human beings. To better understand the underlying neurocognitive mechanism while making decisions under an economic context, we designed a decision-making paradigm based on the newsvendor problem (NP) with two scenarios: low-profit margins as the more challenging scenario and high-profit margins as the less difficult one. The EEG signals were acquired from healthy humans while subjects were performing the task. We adopted the Correlated Component Analysis (CorrCA) method to identify linear combinations of EEG channels that maximize the correlation across subjects ([Formula: see text]) or trials ([Formula: see text]). The inter-subject or inter-trial correlation values (ISC or ITC) of the first three components were estimated to investigate the modulation of the task difficulty on subjects' EEG signals and respective correlations. We also calculated the alpha- and beta-band power of the projection components obtained by the CorrCA to assess the brain responses across multiple task periods. Finally, the CorrCA forward models, which represent the scalp projections of the brain activities by the maximally correlated components, were further translated into source distributions of underlying cortical activity using the exact Low Resolution Electromagnetic Tomography Algorithm (eLORETA). Our results revealed strong and significant correlations in EEG signals among multiple subjects and trials during the more difficult decision-making task than the easier one. We also observed that the NP decision-making and feedback tasks desynchronized the normalized alpha and beta powers of the CorrCA components, reflecting the engagement state of subjects. Source localization results furthermore suggested several sources of neural activities during the NP decision-making process, including the dorsolateral prefrontal cortex, anterior PFC, orbitofrontal cortex, posterior cingulate cortex, and somatosensory association cortex.

Neuroscientific Methods for Exploring User Perceptions While Dealing With Mobile Advertising: A Novel and Integrated Approach

Display and native ads represent two of the most widely used digital advertising formats employed by advertisers that aim to grab the attention of online users. In recent years, the native format has become very popular because it relies on deceptive features that make harder the recognition of its advertising nature, reducing avoiding behaviors such as the banner blindness phenomena, traditionally associated to display advertising, and so increasing its advertising effectiveness. The present study, based on a forefront research protocol specifically designed for the advertising research on smartphone devices, aims to investigate through neurophysiological and self-reported measures, the perception of display and native ads placed within article webpages, and to assess the efficacy of an integrated approach. Eye-tracking results showed higher visual attention and longer viewing time associated with native advertisements in comparison to traditional display advertisements, confirming and extending evidence provided by previous research. Despite a significantly higher rate of self-reported advertising intent was detected for articles containing display ads when compared to articles containing native ads, no differences have been found while performing the same comparison for the neurophysiological measures of emotional involvement and approaching motivation of for the self-reported measures of pleasantness and annoyance. Such findings along with the employment of an innovative research protocol, contribute to providing further cues to the current debate related to the effectiveness of two of the most widely used digital advertising formats.

Effectiveness of Electricity-Saving Communication Campaigns: Neurophysiological Approach

Public communication campaigns are among the tools for promoting electricity saving. A crucial task in the process of creating a campaign is to design a simple message to effectively reach the average consumer. It is a beneficial practice to create alternative messages and pretest them to find the most effective. The research methodology during pretesting includes both quantitative and qualitative methods. However, it is believed that the outcomes obtained with the use of conventional techniques are not fully reliable. Therefore, the following question arises: What additional research methods should be applied at the stage of testing the message of a communication campaign so that its effectiveness can be assessed more reliably and/or improved even before its broadcast? In this study, we aim to present the possibility of applying cognitive neuroscience methods in conjunction with a questionnaire to experimentally check the effectiveness of the message using the example of selected electricity-saving communication campaigns. The key results of this study indicate that merging conscious and subconscious reactions to media messages allows us to gain new knowledge that can be used in the future to improve the communication campaign effectiveness. Our investigation showed the benefits that can be obtained by synergizing traditional research methods with neuroscientific approaches.

Neuroimaging Techniques in Advertising Research: Main Applications, Development, and Brain Regions and Processes

Despite the advancement in neuroimaging tools, studies about using neuroimaging tools to study the impact of advertising on brain regions and processes are scant and remain unclear in academic literature. In this article, we have followed a literature review methodology and a bibliometric analysis to select empirical and review papers that employed neuroimaging tools in advertising campaigns and to understand the global research trends in the neuromarketing domain. We extracted and analyzed sixty-three articles from the Web of Science database to answer our study questions. We found four common neuroimaging techniques employed in advertising research. We also found that the orbitofrontal cortex (OFC), the ventromedial prefrontal cortex, and the dorsolateral prefrontal cortex play a vital role in decision-making processes. The OFC is linked to positive valence, and the lateral OFC and left dorsal anterior insula related in negative valence. In addition, the thalamus and primary visual area associated with the bottom-up attention system, whereas the top-down attention system connected to the dorsolateral prefrontal cortex, parietal cortex, and primary visual areas. For memory, the hippocampus is responsible for generating and processing memories. We hope that this study provides valuable insights about the main brain regions and processes of interest for advertising.

Cognitive Neuroscience Methods in Enhancing Health Literacy

The aim of the article is to identify the usefulness of cognitive neuroscience methods in assessing the effectiveness of social advertising and constructing messages referring to the generally understood health promotion, which is to contribute to the development of health awareness, and hence to health literacy. The presented research has also proven useful in the field of managing the processes that improve the communication between the organization and its environment. The researchers experimentally applied cognitive neuroscience methods, mainly EEG measurements, including a metric which is one of the most frequently used to measure the reception of advertising messages, i.e., frontal asymmetry. The purpose of the study was to test cognitive responses as expressed by neural indices (memorization, interest) to the reception of an advertisement for the construction of a hospice for adults. For comparative purposes, a questionnaire survey was also conducted. The research findings have confirmed that there are significant differences in remembering the advertisement in question by different groups of recipients (women/men). They also indicate a different level of interest in the advertisement, which may result from different preferences of the recipients concerning the nature of ads. The obtained results contribute to a better understanding of how to design advertising messages concerning health, so that they increase the awareness of the recipients’ responsibility for their own health and induce specific behavior patterns aimed at supporting health-related initiatives, e.g., donating funds for building hospices or performing preventive tests. In this respect, the study findings help improve the organizations’ communication with their environment, thus enhancing their performance. The study has also confirmed the potential and innovativeness of cognitive neuroscience methods as well as their considerable possibilities for application in this field.

Touching to Feel: Brain Activity During In-Store Consumer Experience

To gain a deeper understanding of consumers' brain responses during a real-time in-store exploration could help retailers to get much closer to costumers' experience. To our knowledge, this is the first time the specific role of touch has been investigated by means of a neuroscientific approach during consumer in-store experience within the field of sensory marketing. This study explores the presence of distinct cortical brain oscillations in consumers' brain while navigating a store that provides a high level of sensory arousal and being allowed or not to touch products. A 16-channel wireless electroencephalogram (EEG) was applied to 23 healthy participants (mean age = 24.57 years, SD = 3.54), with interest in cosmetics but naive about the store explored. Subjects were assigned to two experimental conditions based on the chance of touching or not touching the products. Cortical oscillations were explored by means of power spectral analysis of the following frequency bands: delta, theta, alpha, and beta. Results highlighted the presence of delta, theta, and beta bands within the frontal brain regions during both sensory conditions. The absence of touch was experienced as a lack of perception that needs cognitive control, as reflected by Delta and Theta band left activation, whereas a right increase of Beta band for touch condition was associated with sustained awareness on the sensory experience. Overall, EEG cortical oscillations' functional meaning could help highlight the neurophysiological implicit responses to tactile conditions and the importance of touch integration in consumers' experience.

A Literature Review of EEG-Based Affective Computing in Marketing

Affect plays an important role in the consumer decision-making process and there is growing interest in the development of new technologies and computational approaches that can interpret and recognize the affects of consumers, with benefits for marketing described in relation to both academia and industry. From an interdisciplinary perspective, this paper aims to review past studies focused on electroencephalography (EEG)-based affective computing (AC) in marketing, which provides a promising avenue for studying the mechanisms underlying affective states and developing recognition computational models to predict the psychological responses of customers. This review offers an introduction to EEG technology and an overview of EEG-based AC; provides a snapshot of the current state of the literature. It briefly presents the themes, challenges, and trends in studies of affect evaluation, recognition, and classification; and further proposes potential guidelines for researchers and marketers.

Opening the 'Black Box': An Overview of Methods to Investigate the Decision-Making Process in Choice-Based Surveys

The desire to understand the preferences of patients, healthcare professionals and the public continues to grow. Health valuation studies, often in the form of discrete choice experiments, a choice based survey approach, proliferate as a result. A variety of methods of pre-choice process analysis have been developed to investigate how and why people make their decisions in such experiments and surveys. These techniques have been developed to investigate how people acquire and process information and make choices. These techniques offer the potential to test and improve theories of choice and/or associated empirical models. This paper provides an overview of such methods, with the focus on their use in stated choice-based healthcare studies. The methods reviewed are eye tracking, mouse tracing, brain imaging, deliberation time analysis and think aloud. For each method, we summarise the rationale, implementation, type of results generated and associated challenges, along with a discussion of possible future developments.

Spectral features based convolutional neural network for accurate and prompt identification of schizophrenic patients

Schizophrenia is a fatal mental disorder, which affects millions of people globally by the disturbance in their thinking, feeling and behaviour. In the age of the internet of things assisted with cloud computing and machine learning techniques, the computer-aided diagnosis of schizophrenia is essentially required to provide its patients with an opportunity to own a better quality of life. In this context, the present paper proposes a spectral features based convolutional neural network (CNN) model for accurate identification of schizophrenic patients using spectral analysis of multichannel EEG signals in real-time. This model processes acquired EEG signals with filtering, segmentation and conversion into frequency domain. Then, given frequency domain segments are divided into six distinct spectral bands like delta, theta-1, theta-2, alpha, beta and gamma. The spectral features including mean spectral amplitude, spectral power and Hjorth descriptors (Activity, Mobility and Complexity) are extracted from each band. These features are independently fed to the proposed spectral features-based CNN and long short-term memory network (LSTM) models for classification. This work also makes use of raw time-domain and frequency-domain EEG segments for classification using temporal CNN and spectral CNN models of same architectures respectively. The overall analysis of simulation results of all models exhibits that the proposed spectral features based CNN model is an efficient technique for accurate and prompt identification of schizophrenic patients among healthy individuals with average classification accuracies of 94.08% and 98.56% for two different datasets with optimally small classification time.

Technological advancements and opportunities in Neuromarketing: a systematic review

Neuromarketing has become an academic and commercial area of interest, as the advancements in neural recording techniques and interpreting algorithms have made it an effective tool for recognizing the unspoken response of consumers to the marketing stimuli. This article presents the very first systematic review of the technological advancements in Neuromarketing field over the last 5 years. For this purpose, authors have selected and reviewed a total of 57 relevant literatures from valid databases which directly contribute to the Neuromarketing field with basic or empirical research findings. This review finds consumer goods as the prevalent marketing stimuli used in both product and promotion forms in these selected literatures. A trend of analyzing frontal and prefrontal alpha band signals is observed among the consumer emotion recognition-based experiments, which corresponds to frontal alpha asymmetry theory. The use of electroencephalogram (EEG) is found favorable by many researchers over functional magnetic resonance imaging (fMRI) in video advertisement-based Neuromarketing experiments, apparently due to its low cost and high time resolution advantages. Physiological response measuring techniques such as eye tracking, skin conductance recording, heart rate monitoring, and facial mapping have also been found in these empirical studies exclusively or in parallel with brain recordings. Alongside traditional filtering methods, independent component analysis (ICA) was found most commonly in artifact removal from neural signal. In consumer response prediction and classification, Artificial Neural Network (ANN), Support Vector Machine (SVM) and Linear Discriminant Analysis (LDA) have performed with the highest average accuracy among other machine learning algorithms used in these literatures. The authors hope, this review will assist the future researchers with vital information in the field of Neuromarketing for making novel contributions.

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

BACKGROUND

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

METHODS

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

RESULTS

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

CONCLUSION

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

Cost-Effective CNNs for Real-Time Micro-Expression Recognition

Micro-Expression (ME) recognition is a hot topic in computer vision as it presents a gateway to capture and understand daily human emotions. It is nonetheless a challenging problem due to ME typically being transient (lasting less than 200 ms) and subtle. Recent advances in machine learning enable new and effective methods to be adopted for solving diverse computer vision tasks. In particular, the use of deep learning techniques on large datasets outperforms classical approaches based on classical machine learning which rely on hand-crafted features. Even though available datasets for spontaneous ME are scarce and much smaller, using off-the-shelf Convolutional Neural Networks (CNNs) still demonstrates satisfactory classification results. However, these networks are intense in terms of memory consumption and computational resources. This poses great challenges when deploying CNN-based solutions in many applications, such as driver monitoring and comprehension recognition in virtual classrooms, which demand fast and accurate recognition. As these networks were initially designed for tasks of different domains, they are over-parameterized and need to be optimized for ME recognition. In this paper, we propose a new network based on the well-known ResNet18 which we optimized for ME classification in two ways. Firstly, we reduced the depth of the network by removing residual layers. Secondly, we introduced a more compact representation of optical flow used as input to the network. We present extensive experiments and demonstrate that the proposed network obtains accuracies comparable to the state-of-the-art methods while significantly reducing the necessary memory space. Our best classification accuracy was 60.17% on the challenging composite dataset containing five objectives classes. Our method takes only 24.6 ms for classifying a ME video clip (less than the occurrence time of the shortest ME which lasts 40 ms). Our CNN design is suitable for real-time embedded applications with limited memory and computing resources.

Neuroergonomic Assessment of Hot Beverage Preparation and Consumption: An EEG and EDA Study

Neuroergonomics is an emerging field that investigates the human brain about behavioral performance in natural environments and everyday settings. This study investigated the body and brain activity correlates of a typical daily activity, hot beverage preparation, and consumption in a realistic office environment where participants performed natural daily tasks. Using wearable, battery operated and wireless Electroencephalogram (EEG) and Electrodermal activity (EDA) sensors, neural and physiological responses were measured in untethered, freely moving participants who prepared hot beverages using two different machines (a market leader and follower as determined by annual US sales). They later consumed the drinks they had prepared in three blocks. Emotional valence was estimated using frontal asymmetry in EEG alpha band power and emotional arousal was estimated from EDA tonic and phasic activity. Results from 26 participants showed that the market-leading coffee machine was more efficient to use based on self-reports, behavioral performance measures, and there were significant within-subject differences in valence between the two machine use. Moreover, the market leader user interface led to greater self-reported product preference, which was further supported by significant differences in measured arousal and valence (EDA and EEG, respectively) during coffee production and consumption. This is the first study that uses a multimodal and comprehensive assessment of coffee machine use and beverage consumption in a naturalistic work environment. Approaches described in this study can be adapted in the future to other task-specific machine usability and consumer neuroscience studies.

Towards emerging EEG applications: a novel printable flexible Ag/AgCl dry electrode array for robust recording of EEG signals at forehead sites

OBJECTIVES

With the rapid development of EEG-based wearable healthcare devices and brain-computer interfaces, reliable and user-friendly EEG sensors for EEG recording, especially at forehead sites, are highly desirable and challenging. However, existing EEG sensors cannot meet the requirements, since wet electrodes require tedious setup and conductive pastes or gels, and most dry electrodes show unacceptable high contact impedance. In addition, the existing electrodes cannot absorb sweat effectively; sweat would cause cross-interferences, and even short circuits, between adjacent electrodes, especially in the moving scenarios, or a hot and humid environment. To resolve these problems, a novel printable flexible Ag/AgCl dry electrode array was developed for EEG acquisition at forehead sites, mainly consisting of screen printing the Ag/AgCl coating, conductive sweat-absorbable sponges and flexible tines.

APPROACH

A systematic method was also established to evaluate the flexible dry electrode array.

MAIN RESULTS

The experimental results show the flexible dry electrode array has reproducible electrode potential, relatively low electrode-skin impedance, and good stability. Moreover, the EEG signals can be effectively captured with a high quality that is comparable to that of wet electrodes.

SIGNIFICANCE

All the results confirmed the feasibility of forehead EEG recording in real-world scenarios using the proposed flexible dry electrode array, with a rapid and facile operation as well as the advantages of self-application, user-friendliness and wearer comfort.

Consumer Behaviour through the Eyes of Neurophysiological Measures: State-of-the-Art and Future Trends

The new technological advances achieved during the last decade allowed the scientific community to investigate and employ neurophysiological measures not only for research purposes but also for the study of human behaviour in real and daily life situations. The aim of this review is to understand how and whether neuroscientific technologies can be effectively employed to better understand the human behaviour in real decision-making contexts. To do so, firstly, we will describe the historical development of neuromarketing and its main applications in assessing the sensory perceptions of some marketing and advertising stimuli. Then, we will describe the main neuroscientific tools available for such kind of investigations (e.g., measuring the cerebral electrical or hemodynamic activity, the eye movements, and the psychometric responses). Also, this review will present different brain measurement techniques, along with their pros and cons, and the main cerebral indexes linked to the specific mental states of interest (used in most of the neuromarketing research). Such indexes have been supported by adequate validations from the scientific community and are largely employed in neuromarketing research. This review will also discuss a series of papers that present different neuromarketing applications, such us in-store choices and retail, services, pricing, brand perception, web usability, neuropolitics, evaluation of the food and wine taste, and aesthetic perception of artworks. Furthermore, this work will face the ethical issues arisen on the use of these tools for the evaluation of the human behaviour during decision-making tasks. In conclusion, the main challenges that neuromarketing is going to face, as well as future directions and possible scenarios that could be derived by the use of neuroscience in the marketing field, will be identified and discussed.

A Neuroscientific Approach to Explore Consumers’ Intentions Towards Sustainability within the Luxury Fashion Industry

Little is presently known about customers’ expectations and the unspoken relevant factors which lead them to prefer or not sustainable luxury products. This study aimed to deepen the understanding of luxury consumers’ implicit intentions towards sustainability by using, for the first time, a neuroscientific approach applied to the luxury fashion domain. A greater cortical activity related to cognitive and emotional aspects was hypothesized for luxury sustainability-oriented consumers than for non-sustainability-oriented subjects when presented with sustainability-related cues. Sixteen luxury consumers were divided into two groups according to their sensitivity towards sustainability issues. They were asked to observe a set of 10 stimuli depicting sustainability issues and then to interact with a salesperson while their cortical activity was recorded by an electroencephalogram (EEG). Frequency band analysis revealed higher levels of beta, delta, and theta band EEG activity in temporoparietal than frontocentral areas when observing pictures related to sustainability and a specific right temporoparietal theta band activation for the Nonsustainable Group. An increased level of knowledge of sustainability themes was confirmed by the correct detection of stimuli valence and a significant presence of delta power when the salesperson explained the brand’s sustainable policy. The specific brain responses related to sensitivity towards sustainability and the different effect of knowledge on sustainability topics based on group differences are discussed here in light of emotional behavior.

Consumer Neuroscience and Digital/Social Media Health/Social Cause Advertisement Effectiveness

This research investigated the use of consumer neuroscience to improve and determine the effectiveness of action/emotion-based public health and social cause (HSC) advertisements. Action-based advertisements ask individuals to 'do something' such as 'act', 'share', make a 'pledge' or complete a 'challenge' on behalf of a brand, such as doing 'something good, somewhere, for someone else'. Public health messages as noncommercial advertisements attempt to positively change behavioural intent or increase awareness. Australian health expenditure was $180.7 billion AUD (Australian dollars) in 2016/17 with $17 million AUD spent on government health campaigns. However, evaluation of health advertisement effectiveness has been difficult to determine. Few studies use neuroscience techniques with traditional market research methods. A 2-part study with an exploratory design was conducted using (1) electroencephalography (EEG) using a 64 channel EEG wet cap (n = 47); and (2) a Qualtrics online psychometric survey (n = 256). Participants were asked to make a donation before and after viewing 7 HSC digital/social media advertisements and logos (6 action/emotion-based; 1 control) to measure changes in behavioural intent. Attention is considered a key factor in determining advertising effectiveness. EEG results showed theta synchronisation (increase)/alpha desynchronisation (decrease) indicating attention with episodic memory encoding. sLORETA results displayed approach responses to action/emotion-based advertisements with left prefrontal and right parietal cortex activation. EEG and survey results showed the greatest liking for the ManUp action/emotion-based advertisement which used male facial expressions of raw emotion and vulnerability. ManUp also had the highest increased amount donated after viewing. Lower theta amplitude results for the International Fund for Animal Welfare (IFAW) action/emotion-based advertisement indicated that novel (possessing distinct features) rather than attractive/conventional faces were more appealing, while the rapid presentation of faces was less effective. None of the highest peak amplitudes for each ad occurred when viewing brand logos within the advertisement. This research contributes to the academic consumer neuroscience, advertising effectiveness, and social media literature with the use of action/challenge/emotion-based marketing strategies, which remains limited, while demonstrating the value in combining EEG and neuroscientific techniques with traditional market research methods. The research provides a greater understanding of advertising effectiveness and changes in behavioural intent with managerial implications regarding the effective use of action/challenge/emotion-based HSC communications to potentially help save a life and reduce expenditure on ineffectual HSC marketing campaigns.

Individual EEG measures of attention, memory, and motivation predict population level TV viewership and Twitter engagement

Television (TV) programming attracts ever-growing audiences and dominates the cultural zeitgeist. Viewership and social media engagement have become standard indices of programming success. However, accurately predicting individual episode success or future show performance using traditional metrics remains a challenge. Here we examine whether TV viewership and Twitter activity can be predicted using electroencephalography (EEG) measures, which are less affected by reporting biases and which are commonly associated with different cognitive processes. 331 participants watched an hour-long episode from one of nine prime-time shows (~36 participants per episode). Three frequency-based measures were extracted: fronto-central alpha/beta asymmetry (indexing approach motivation), fronto-central alpha/theta power (indexing attention), and fronto-central theta/gamma power (indexing memory processing). All three EEG measures and the composite EEG score significantly correlated across episode segments with the two behavioral measures of TV viewership and Twitter volume. EEG measures explained more variance than either of the behavioral metrics and mediated the relationship between the two. Attentional focus was integral for both audience retention and Twitter activity, while emotional motivation was specifically linked with social engagement and program segments with high TV viewership. These findings highlight the viability of using EEG measures to predict success of TV programming and identify cognitive processes that contribute to audience engagement with television shows.

Neurophysiological Profile of Antismoking Campaigns

Over the past few decades, antismoking public service announcements (PSAs) have been used by governments to promote healthy behaviours in citizens, for instance, against drinking before the drive and against smoke. Effectiveness of such PSAs has been suggested especially for young persons. By now, PSAs efficacy is still mainly assessed through traditional methods (questionnaires and metrics) and could be performed only after the PSAs broadcasting, leading to waste of economic resources and time in the case of Ineffective PSAs. One possible countermeasure to such ineffective use of PSAs could be promoted by the evaluation of the cerebral reaction to the PSA of particular segments of population (e.g., old, young, and heavy smokers). In addition, it is crucial to gather such cerebral activity in front of PSAs that have been assessed to be effective against smoke (Effective PSAs), comparing results to the cerebral reactions to PSAs that have been certified to be not effective (Ineffective PSAs). The eventual differences between the cerebral responses toward the two PSA groups will provide crucial information about the possible outcome of new PSAs before to its broadcasting. This study focused on adult population, by investigating the cerebral reaction to the vision of different PSA images, which have already been shown to be Effective and Ineffective for the promotion of an antismoking behaviour. Results showed how variables as gender and smoking habits can influence the perception of PSA images, and how different communication styles of the antismoking campaigns could facilitate the comprehension of PSA's message and then enhance the related impact.

Neurophysiological Measures of the Perception of Antismoking Public Service Announcements Among Young Population

Tobacco constitutes a global emergency with totally preventable millions of deaths per year and smoking-related illnesses. Public service announcements (PSAs) are the main tool against smoking and by now their efficacy is still assessed through questionnaires and metrics, only months after their circulation. The present study focused on the young population, because at higher risk of developing tobacco addiction, investigating the reaction to the vision of Effective, Ineffective and Awarded antismoking PSAs through: electroencephalography (EEG), autonomic activity variation (Galvanic skin response—GSR- and Heart Rate—HR-) and Eye-Tracking (ET). The employed indices were: the EEG frontal alpha band asymmetry and the frontal theta; the Emotional Index (EI), deriving from the GSR and HR signals matching; the ET Visual Attention (VA) index, based on the ratio between the total time spent fixating an area of interest (AOI) and its area. Smokers expressed higher frontal alpha asymmetry values in comparison to non-smokers. Concerning frontal theta, Awarded PSAs reported the highest values in comparison to both Effective and Ineffective PSAs. EI results highlighted that lowest values were expressed by Heavy Smokers (HS), and Effective PSAs obtained the highest EI values. Finally, concerning the Effective PSAs, regression analysis highlighted a correlation between the number of cigarettes smoked by participants (independent variable) and frontal alpha asymmetry, frontal theta and EI values. ET results suggested that for the Ineffective PSAs the main focus were texts, while for the Effective and Awarded PSAs were the visual elements. Results support the use of methods aimed at assessing the physiological reaction for the evaluation of PSAs images, in particular when considering the smoking habits of target populations.

SEREEGA: Simulating event-related EEG activity

BACKGROUND

Electroencephalography (EEG) is a popular method to monitor brain activity, but it is difficult to evaluate EEG-based analysis methods because no ground-truth brain activity is available for comparison. Therefore, in order to test and evaluate such methods, researchers often use simulated EEG data instead of actual EEG recordings. Simulated data can be used, among other things, to assess or compare signal processing and machine learning algorithms, to model EEG variabilities, and to design source reconstruction methods.

NEW METHOD

We present SEREEGA, Simulating Event-Related EEG Activity. SEREEGA is a free and open-source MATLAB-based toolbox dedicated to the generation of simulated epochs of EEG data. It is modular and extensible, at initial release supporting five different publicly available head models and capable of simulating multiple different types of signals mimicking brain activity. This paper presents the architecture and general workflow of this toolbox, as well as a simulated data set demonstrating some of its functions. The toolbox is available at https://github.com/lrkrol/SEREEGA.

RESULTS

The simulated data allows established analysis pipelines and classification methods to be applied and is capable of producing realistic results.

COMPARISON WITH EXISTING METHODS

Most simulated EEG is coded from scratch. The few open-source methods in existence focus on specific applications or signal types, such as connectivity. SEREEGA unifies the majority of past simulation methods reported in the literature into one toolbox.

CONCLUSION

SEREEGA is a general-purpose toolbox to simulate ground-truth EEG data.

Neuronal Correlates of Product Feature Attractiveness

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

When a new technological product launching fails: A multi-method approach of facial recognition and E-WOM sentiment analysis

The dual aim of this research is, firstly, to analyze the physiological and unconscious emotional response of consumers to a new technological product and, secondly, link this emotional response to consumer conscious verbal reports of positive and negative product perceptions. In order to do this, biometrics and self-reported measures of emotional response are combined. On the one hand, a neuromarketing experiment based on the facial recognition of emotions of 10 subjects, when physical attributes and economic information of a technological product are exposed, shows the prevalence of the ambivalent emotion of surprise. On the other hand, a nethnographic qualitative approach of sentiment analysis of 67-user online comments characterise the valence of this emotion as mainly negative in the case and context studied. Theoretical, practical and methodological contributions are anticipated from this paper. From a theoretical point of view this proposal contributes valuable information to the product design process, to an effective development of the marketing mix variables of price and promotion, and to a successful selection of the target market. From a practical point of view, the approach employed in the case study on the product Google Glass provides empirical evidence useful in the decision making process for this and other technological enterprises launching a new product. And from a methodological point of view, the usefulness of integrated neuromarketing-eWOM analysis could contribute to the proliferation of this tandem in marketing research.

Alpha EEG Frontal Asymmetries during Audiovisual Perception in Cochlear Implant Users

Objectives: The aim of the present study is to investigate the variations of the electroencephalographic (EEG) alpha rhythm in order to measure the appreciation of bilateral and unilateral young cochlear implant users during the observation of a musical cartoon. The cartoon has been modified for the generation of three experimental conditions: one with the original audio, another one with a distorted sound and, finally, a mute version.

Methods: The EEG data have been recorded during the observation of the cartoons in the three experimental conditions. The frontal alpha EEG imbalance has been calculated as a measure of motivation and pleasantness to be compared across experimental populations and conditions.

Results: The EEG frontal imbalance of the alpha rhythm showed significant variations during the perception of the different cartoons. In particular, the pattern of activation of normal-hearing children is very similar to the one elicited by the bilateral implanted patients. On the other hand, results related to the unilateral subjects do not present significant variations of the imbalance index across the three cartoons.

Conclusion: The presented results suggest that the unilateral patients could not appreciate the difference in the audio format as well as bilaterally implanted and normal hearing subjects. The frontal alpha EEG imbalance is a useful tool to detect the differences in the appreciation of audiovisual stimuli in cochlear implant patients.

Brain Activation in Response to Personalized Behavioral and Physiological Feedback From Self-Monitoring Technology: Pilot Study

Background

The recent surge in commercially available wearable technology has allowed real-time self-monitoring of behavior (eg, physical activity) and physiology (eg, glucose levels). However, there is limited neuroimaging work (ie, functional magnetic resonance imaging [fMRI]) to identify how people’s brains respond to receiving this personalized health feedback and how this impacts subsequent behavior.

Objective

Identify regions of the brain activated and examine associations between activation and behavior.

Methods

This was a pilot study to assess physical activity, sedentary time, and glucose levels over 14 days in 33 adults (aged 30 to 60 years). Extracted accelerometry, inclinometry, and interstitial glucose data informed the construction of personalized feedback messages (eg, average number of steps per day). These messages were subsequently presented visually to participants during fMRI. Participant physical activity levels and sedentary time were assessed again for 8 days following exposure to this personalized feedback.

Results

Independent tests identified significant activations within the prefrontal cortex in response to glucose feedback compared with behavioral feedback (P<.001). Reductions in mean sedentary time (589.0 vs 560.0 minutes per day, P=.014) were observed. Activation in the subgyral area had a moderate correlation with minutes of moderate-to-vigorous physical activity (r=0.392, P=.043).

Conclusion

Presenting personalized glucose feedback resulted in significantly more brain activation when compared with behavior. Participants reduced time spent sedentary at follow-up. Research on deploying behavioral and physiological feedback warrants further investigation.

Electroencephalographic, Heart Rate, and Galvanic Skin Response Assessment for an Advertising Perception Study: Application to Antismoking Public Service Announcements

The evaluation of advertising, products, and packaging is traditionally performed through methods based on self-reports and focus groups, but these approaches often appear poorly accurate in scientific terms. Neuroscience is increasingly applied to the investigation of the neurophysiological bases of the perception of and reaction to commercial stimuli to support traditional marketing methods. In this context, a particular sector or marketing is represented by public service announcements (PSAs). The objective of this protocol is to apply electroencephalography (EEG) and autonomic signal analysis to study responses to selected antismoking PSAs. Two EEG indices were employed: the frontal alpha band EEG asymmetry (the Approach Withdrawal (AW) index) and the frontal theta (effort index). Furthermore, the autonomic Emotional Index (EI) was calculated, as derived from the Galvanic Skin Response (GSR) and Heart Rate (HR) signals. The present protocol describes a series of operational and computational steps required to properly estimate, through the aforementioned indices, the emotional and cerebral reaction of a group of subjects towards a selected number of antismoking PSAs. In particular, a campaign characterized by a symbolic communication style (classified as "awarded" on the basis of the prizes received by specialized committees) obtained the highest approach values, as estimated by the AW index. A spot and an image belonging to the same PSA campaign based on the "fear arousing appeal" and with a narrative/experiential communication style (classified as "effective" on the basis of the economical/health-related improvements promoted) reported the lowest and highest effort values, respectively. This is probably due to the complexity of the storytelling (spot) and to the immediateness of the image (a lady who underwent a tracheotomy). Finally, the same "effective" campaign showed the highest EI values, possibly because of the empathy induced by the testimonial and the explicitness of the message.

A Neuroelectrical Brain Imaging Study on the Perception of Figurative Paintings against Only their Color or Shape Contents

In this study, the cortical activity correlated with the perception and appreciation of different set of pictures was estimated by using neuroelectric brain activity and graph theory methodologies in a group of artistic educated persons. The pictures shown to the subjects consisted of original pictures of Titian's and a contemporary artist's paintings (Orig dataset) plus two sets of additional pictures. These additional datasets were obtained from the previous paintings by removing all but the colors or the shapes employed (Color and Style dataset, respectively). Results suggest that the verbal appreciation of Orig dataset when compared to Color and Style ones was mainly correlated to the neuroelectric indexes estimated during the first 10 s of observation of the pictures. Always in the first 10 s of observation: (1) Orig dataset induced more emotion and is perceived with more appreciation than the other two Color and Style datasets; (2) Style dataset is perceived with more attentional effort than the other investigated datasets. During the whole period of observation of 30 s: (1) emotion induced by Color and Style datasets increased across the time while that induced of the Orig dataset remain stable; (2) Color and Style dataset were perceived with more attentional effort than the Orig dataset. During the entire experience, there is evidence of a cortical flow of activity from the parietal and central areas toward the prefrontal and frontal areas during the observation of the images of all the datasets. This is coherent from the notion that active perception of the images with sustained cognitive attention in parietal and central areas caused the generation of the judgment about their aesthetic appreciation in frontal areas.

A pilot study on the neurometric evaluation of "effective" and "ineffective" antismoking public service announcements

Tobacco use is the leading cause of preventable death and smoking-related illness worldwide. Research has shown that antismoking advertising may help reduce this habit. Nowadays, public service announcements (PSAs) are considered "Effective" or "Ineffective" on the base of official reports concerning behavioral/attitudinal changes toward healthier patterns and health-related savings following the exposure to the PSA. In this pilot study, we described the results of the use of three neurometric indexes for the evaluation of the efficacy of a couple of antismoking PSAs in a reduced sample of voluntary participants. The study applied the gathering of the electroencephalographic (EEG) rhythms variations, as well as the heart rate (HR) and galvanic skin response (GSR). The neurometric indicators here employed were the Approach-Withdrawal (AW), the Effort (EfI) and the Emotional (EI) indexes. Results suggest a significant higher values for AW, Effort and Emotional indexes (p=0,02; p= 0,03 and p= 0,01 respectively) related to the perception of the "Effective" antismoking PSAs against the perception of the "Ineffective" one. Since this is a pilot study, the results obtained need further investigation, in terms of enlarged stimuli sample and number of participants to provide indications concerning the relevant features to be included in the realization of effective anti-smoking PSAs.

EEG Spectral Dynamics of Video Commercials: Impact of the Narrative on the Branding Product Preference

Neuromarketing has become popular and received a lot of attention. The quality of video commercials and the product information they convey to consumers is a hotly debated topic among advertising agencies and product advertisers. This study explored the impact of advertising narrative and the frequency of branding product exposures on the preference for the commercial and the branding product. We performed electroencephalography (EEG) experiments on 30 subjects while they watched video commercials. The behavioral data indicated that commercials with a structured narrative and containing multiple exposures of the branding products had a positive impact on the preference for the commercial and the branding product. The EEG spectral dynamics showed that the narratives of video commercials resulted in higher theta power of the left frontal, bilateral occipital region, and higher gamma power of the limbic system. The narratives also induced significant cognitive integration-related beta and gamma power of the bilateral temporal regions and the parietal region. It is worth noting that the video commercials with a single exposure of the branding products would be indicators of attention. These new findings suggest that the presence of a narrative structure in video commercials has a critical impact on the preference for branding products.

Inteligencia emocional y percepción de las emociones básicas como un probable factor contribuyente al mejoramiento del rendimiento en las ventas: Una investigación teórica

La inteligencia emocional es uno de los conceptos que ha empezado a transformar el mundo empresarial gracias a los avances en el reconocimiento y el manejo de las emociones. Este es un campo que permitirá la elaboración de estrategias para robustecer los procesos de desarrollo organizacional. El presente artículo de investigación bibliográfica, sistematiza y aporta a la producción científica sobre el concepto de inteligencia emocional y sus potenciales aplicaciones al proceso de ventas. El objetivo de este estudio fue el de conceptualizar acerca del rol que puede jugar la percepción de los distintos estados emocionales en el proceso de ventas y cómo esto puede ayudar a mejorar el rendimiento empresarial.

Cue Reactivity in Nicotine and Alcohol Addiction: A Cross-Cultural View

A wealth of research indicates that cue reactivity is critical to understanding the neurobiology of nicotine and alcohol addiction and developing treatments. Functional magnetic resonance imaging (fMRI) and electroencephalograph (EEG) studies have shown abnormal cue reactivity in various conditions between nicotine or alcohol addicts and the healthy. Although the causes of these abnormalities are still unclear, cultural effect can not be ignored. We conduct an review of fMRI and EEG studies about the cue reactivity in nicotine and alcohol addiction and highlight the cultural perspective. We suggest that cultural cue reactivity is a field worth of exploring which may has an effect on addictive behavior through emotion and attention. The cultural role of nicotine and alcohol addiction would provide new insight into understanding the mechanisms of nicotine and alcohol addiction and developing culture-specific therapies. We consider that culture as a context may be a factor that causes confusing outcomes in exploring nicotine and alcohol addiction which makes it possible to control the cultural influences and further contribute to the more consistent results.

Discrete classification technique applied to TV advertisements liking recognition system based on low-cost EEG headsets

Background

In this paper a new approach is applied to the area of marketing research. The aim of this paper is to recognize how brain activity responds during the visualization of short video advertisements using discrete classification techniques. By means of low cost electroencephalography devices (EEG), the activation level of some brain regions have been studied while the ads are shown to users. We may wonder about how useful is the use of neuroscience knowledge in marketing, or what could provide neuroscience to marketing sector, or why this approach can improve the accuracy and the final user acceptance compared to other works.

Methods

By using discrete techniques over EEG frequency bands of a generated dataset, C4.5, ANN and the new recognition system based on Ameva, a discretization algorithm, is applied to obtain the score given by subjects to each TV ad.

Results

The proposed technique allows to reach more than 75 % of accuracy, which is an excellent result taking into account the typology of EEG sensors used in this work. Furthermore, the time consumption of the algorithm proposed is reduced up to 30 % compared to other techniques presented in this paper.

Conclusions

This bring about a battery lifetime improvement on the devices where the algorithm is running, extending the experience in the ubiquitous context where the new approach has been tested.

A Comparison of Physiological Signal Analysis Techniques and Classifiers for Automatic Emotional Evaluation of Audiovisual Contents

This work focuses on finding the most discriminatory or representative features that allow to classify commercials according to negative, neutral and positive effectiveness based on the Ace Score index. For this purpose, an experiment involving forty-seven participants was carried out. In this experiment electroencephalography (EEG), electrocardiography (ECG), Galvanic Skin Response (GSR) and respiration data were acquired while subjects were watching a 30-min audiovisual content. This content was composed by a submarine documentary and nine commercials (one of them the ad under evaluation). After the signal pre-processing, four sets of features were extracted from the physiological signals using different state-of-the-art metrics. These features computed in time and frequency domains are the inputs to several basic and advanced classifiers. An average of 89.76% of the instances was correctly classified according to the Ace Score index. The best results were obtained by a classifier consisting of a combination between AdaBoost and Random Forest with automatic selection of features. The selected features were those extracted from GSR and HRV signals. These results are promising in the audiovisual content evaluation field by means of physiological signal processing.