A LSTM based deep learning network for recognizing emotions using wireless brainwave driven system

Abstract

Positive and Negative emotions are experienced by the majority of individuals in their day-to-day life. It is important to control access of negative emotions because it may lead to several chronic health issues like depression and anxiety. The purpose of this research work is to develop a portable brainwave driven system for recognizing positive, negative, and neutral emotions. This research considers the classification of four negative class of emotions using genres sadness, disgust, angry, and surprise along with the classification of three basic class of emotions i.e., positive, negative, and neutral. 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. The secondary goal is to assess the human behavior of different age groups and gender. We have compared the performance of Multilayer Perceptron (MLP), K-nearest neighbors (KNN), Support Vector Machine (SVM), LIB-Support Vector Machine (LIB-SVM), and LSTM based deep learning model for classification. The analysis shows that, for four class of emotions LSTM based deep learning model provides classification accuracy as 83.12%, 86.94%, 91.67%, and 94.12% for 50–50, 60–40, 70–30, and 10-fold cross-validations. For three class of emotions LSTM based deep learning model provides classification accuracy as 81.33%, 85.41%, 89.44%, and 92.66% for 50–50, 60–40, 70–30, and 10-fold cross-validation. The generalizability and reliability of this approach are evaluated by applying our approach to publicly available EEG datasets DEAP and SEED. In compliance with the self-reported feelings, brain signals of 18–25 years of age group provided the highest emotional identification. The results show that among genders, females are more emotionally active as compared to males. These results affirmed the potential use of our method for recognizing positive, negative, and neutral emotions.