EEG Emotion Recognition Using Dynamical Graph Convolutional Neural Networks

TLDR

The study proposes a multichannel EEG emotion recognition method using a novel dynamical graph convolutional neural network (DGCNN). The DGCNN models EEG channels as a graph, dynamically learns an adjacency matrix to capture inter‑channel relationships, and extracts discriminative features for emotion classification, evaluated on the SEED and DREAMER datasets. The method outperforms state‑of‑the‑art approaches, achieving 90.4 % accuracy in subject‑dependent SEED experiments, 79.95 % in subject‑independent SEED cross‑validation, and 86.23 %, 84.54 %, and 85.02 % for valence, arousal, and dominance on DREAMER.

Abstract

In this paper, a multichannel EEG emotion recognition method based on a novel dynamical graph convolutional neural networks (DGCNN) is proposed. The basic idea of the proposed EEG emotion recognition method is to use a graph to model the multichannel EEG features and then perform EEG emotion classification based on this model. Different from the traditional graph convolutional neural networks (GCNN) methods, the proposed DGCNN method can dynamically learn the intrinsic relationship between different electroencephalogram (EEG) channels, represented by an adjacency matrix, via training a neural network so as to benefit for more discriminative EEG feature extraction. Then, the learned adjacency matrix is used to learn more discriminative features for improving the EEG emotion recognition. We conduct extensive experiments on the SJTU emotion EEG dataset (SEED) and DREAMER dataset. The experimental results demonstrate that the proposed method achieves better recognition performance than the state-of-the-art methods, in which the average recognition accuracy of 90.4 percent is achieved for subject dependent experiment while 79.95 percent for subject independent cross-validation one on the SEED database, and the average accuracies of 86.23, 84.54 and 85.02 percent are respectively obtained for valence, arousal and dominance classifications on the DREAMER database.