Predicting Choices Driven by Emotional Stimuli Using EEG-Based Analysis and Deep Learning

Abstract

Individual choices and preferences are important factors that impact decision making. Artificial intelligence can predict decisions by objectively detecting individual choices and preferences using natural language processing, computer vision, and machine learning. Brain–computer interfaces can measure emotional reactions and identify brain activity changes linked to positive or negative emotions, enabling more accurate prediction models. This research aims to build an individual choice prediction system using electroencephalography (EEG) signals from the Shanghai Jiao Tong University emotion and EEG dataset (SEED). Using EEG, we built different deep learning models, such as a convolutional neural network, long short-term memory (LSTM), and a hybrid model to predict choices driven by emotional stimuli. We also compared their performance with different classical classifiers, such as k-nearest neighbors, support vector machines, and logistic regression. We also utilized ensemble classifiers such as random forest, adaptive boosting, and extreme gradient boosting. We evaluated our proposed models and compared them with previous studies on SEED. Our proposed LSTM model achieved good results, with an accuracy of 96%.