Mental state identification based on the classification of EEG signals

EEG state classification is used in many fields, and decision-making, as a higher cognitive function of the brain, has high research significance, and this paper is mainly to study the state of decision-making. Therefore, we study the classification of three decision states, before-decision, in-decision, post-decision, and two resting states, eye-opened, eye-closed. In this study, three methods are used to compare the classification effects, namely DE+SVM, DE+DGCNN, EEGNet, among which differential entropy (DE) is a frequency domain feature, which can extract effective features in EEG emotion recognition; DGCNN is a dynamic graph convolutional neural network which uses DE as the node feature and dynamically learns the adjacency matrix for classification; EEGNet is an end-to-end neural network, which is designed to be used in multiple experimental paradigms. The above 3 methods achieved 62.80%±9.67%, 78.70%±8.27%, and 88.83±6.03% accuracy in within-subject classification respectively. Finally, we visualize the adjacency matrix learned by DGCNN and the spatial filter learned by EEGNet to see the knowledge learned by the model.