EEG Emotion Realization through Hybrid Network

Abstract

Emotions are essential in interpreting the actions and relations of people. Nowadays, EEG signals are commonly used in emotion realization, so neurologists and psychiatrists can use a brain-computer interface to diagnose and cure emotional disorders. Another benefit of the BCI system is recognizing emotions without a clinical exam. A significant issue in automatic emotion realization is the appropriate extraction and selection of features. This paper aims to develop a state-of-the-art method to automatically classify four types of emotions from PSD and DE features. Features are fed to a convolutional neural network and long short-term memory network (CNN-LSTM). The 1DCNN network has been combined with the LSTM network to get a more stable system. The accuracy of subject-independent emotion realization with PSD and DE features is 84% and 93%, respectively. We have also compared our proposed CNN-LSTM model with other deep learning models. Our results are promising compared to similar approaches and have a better F1 score value. The experimental results also show that human emotions are better recognized by beta and gamma band