Emotion Detection with Single Channel EEG Signal using Deep Learning Algorithm

Abstract

Today’s young generation is tormented by anxiety and stress. A past study shows that, anxiety and stress negatively impact mental and physical health, which ultimately ends up in loss of confidence, self-esteem, and negative performance. This work set guidelines for a replacement approach in neuroscience that only single-channel EEG data has sufficient information for emotion recognition. In this paper, the performance of system is evaluated using subject independent test on the SEED benchmark database using deep learning algorithm namely a bidirectional long short term memory (BiLSTM) classifier. The performance shows that results of single-channel FP1, the combined band (theta, alpha, beta, and gamma) are similar to 62 channels the best accuracy of the beta band. Result obtained for single channel (FP1) using differential entropy (DE) for all band is 74.91% as that of the highest accuracy of the beta band for 62 channels Yang Li, W. Zheng, 2019 74.85%.