Emotion Recognition from EEG Data Using Hybrid Deep Learning Approach

Abstract

Emotion recognition from EEG signals is a challenging task. Researchers over the past few years have been working extensively to achieve high accuracy in emotion recognition from physiological signals. Several feature extraction methods, as well as machine learning models, have been proposed by earlier studies. In this paper, we propose a hybrid CNN-LSTM model for multi-class emotion recognition from EEG signals. The experiment is conducted on the standard benchmark DEAP dataset. The proposed model gives test accuracies of 96.87% and 97.31% on valence and arousal dimensions, respectively. Furthermore, the proposed model also succeeds in achieving state-of-the-art accuracy in both valence and arousal dimensions.