A Long Short Term Memory Deep Learning Network for the Classification of Negative Emotions Using EEG Signals

Abstract

In cognitive science and human-computer interaction, automatic human emotion recognition using physiological stimuli is a key technology. This research considers classification of negative emotions using EEG signals in response to emotional clips. This paper introduces a long short term memory deep learning (LSTM) network to recognize emotions using EEG signals. The primary goal of this approach is to assess the classification performance of the LSTM model for classifying emotions. The secondary goal is to assess the human behavior of different age groups and genders. We have compared the performance of Multilayer Perceptron (MLP), K-nearest neighbors (KNN), Support Vector Machine (SVM), Deep Belief Network based SVM (DBN-SVM), and LSTM based deep learning model for classification of negative emotions using brain signals. The analysis shows that for four class of negative emotion recognition LSTM based deep learning model provides classification accuracy as 81.63%, 84.64%, 89.73%, and 92.84% for 50-50, 60-40, 70-30, and 10-fold cross-validation. Generalizability and reliability of this approach is evaluated by applying our approach to publicly available EEG dataset DEAP and SEED. In compliance with the self-reported feelings, brain signals of 26-35 years of age group provided the highest emotional identification. Among genders, females are more emotionally active as compared to males.