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.

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

Depression is the most common mental illness, which has become the major cause of fear and suicidal mortality or tendencies. Currently, about 10% of the world population has been suffering from depression. The classical approach for detecting depression relies on the clinical questionnaire, which depends on the patients' responses as well as observing their behavioral activities. However, there is no established method to detect depression from EEG biomarkers. Therefore, exploration of EEG biomarkers for depression assessments is vital and has a great potential to improve our understanding and clinical interventions. In this study, we have conducted a systematic review of 52 research articles using the PRISMA-P systematic review protocol, where we analyzed their research methodologies and outcomes. We categorized the experimentations in these articles according to their physical and psychological aspects scaled by the commonly used clinical questionnaire-based assessments. This study finds that the negative stimuli are the better identification strategies for evaluating depression through EEG signals. From this exploration, researchers observed that the Neural Connectivity Analysis and Brain Topological Mapping have huge potentials for finding depression biomarkers, and it is evident that the right-side hemisphere and frontal and parietal-occipital cortex are distinct regions to detect depression using EEG signals. For this mechanism, researchers are using many signal processing and machine learning approaches. In the case of filtering, Independent Component Analysis (ICA) is commonly used to eliminate physiological and non-physiological artifacts. Among machine learning approaches, Convolutional Neural Network (CNN) and Support Vector Machine (SVM) showed better performance for classifying healthy and depressed brains. The authors hope, this study will create an opportunity to explore more in the future for EEG as diagnostic tools by analyzing brain functional connectivity for focusing on clinical interventions.

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