Classification of EEG Signals for Neuromarketing applications

Abstract

Every year, more than 400 billion dollars is spent on marketing campaigns. It is a common practice to promote diverse customer goods through advertising campaigns in order to boost revenues and consumer awareness. The effectiveness of business investments in marketing campaigns is entirely dependent on consumers’ willingness and ability to describe how they feel after watching an advertisement. Conventional marketing techniques (e.g., television commercials and newspaper ads) are unaware of human emotions/response while watching the advertisements. Traditional advertising techniques seek to govern the consumer’s opinion toward a product, which may not reflect their actual behavior at the time of purchase. It is probable that advertisers misjudge consumer behavior because predicted opinions do not always correspond to consumers’ actual purchase behaviors. Neuromarketing is the new paradigm of understanding customer buyer behaviour, decision making as well as the prediction of their gestures for product utilization through an unconscious process. The field of neuromarketing has gained traction as means of bridging the gap between traditional advertising methods that focus on explicit consumer responses, and neuromarketing methodologies that focus on implicit consumer responses. Choice prediction allows it to figure out what buyers really desire about the product. Neuroscience information can be used in neuromarketing to know the behavior of a consumer with the help of brain activity using EEG signals. EEG-based preference recognition systems focus on three key phases. In previous studies, researchers did not focus on effective preprocessing and classification techniques of EEG signals, so in this study, an effective method for preprocessing and classification of EEG signals is proposed, using deep learning to determine the choices of consumers for various products by measuring their “liking” and “disliking” as neuromarketing applications. The proposed method involves effective preprocessing of EEG signals by removing noise and a synthetic minority oversampling technique (SMOTE) to deal with the class imbalance problem. The dataset employed in this study is a publicly available neuromarketing dataset. The dataset consists of EEG data recordings taken from 25 participants, shown different sorts of products. The responses of customers were recorded in terms of likes and dislikes. Automated features were extracted using a long-short term memory network (LSTM) and then concatenated with handcrafted features like power spectral density (PSD) and discrete wavelet transform (DWT) to create a complete feature set. The classification was done using the proposed hybrid classifier that optimizes the weights of two machine learning classifiers and one deep learning classifier and classifies the data between like and dislike. The machine learning classifiers include the Support Vector Machine (SVM), Random Forest (RF), and Deep Learning Classifier (DNN). The proposed hybrid model outperforms and achieves an accuracy of 96.89% among other different classifiers like RF, SVM, and DNN. In the proposed method, accuracy, sensitivity, and specificity were computed to evaluate and compare the proposed method with recent state-of-the-art methods.