AMDET: Attention Based Multiple Dimensions EEG Transformer for Emotion Recognition

Abstract

Affective computing is an important subfield of artificial intelligence, and with the rapid development of brain-computer interface technology, emotion recognition based on EEG signals has received broad attention. It is still a great challenge to effectively explore the multi-dimensional information in the EEG data in spite of a large number of deep learning methods. In this paper, we propose a deep learning model called Attention-based Multiple Dimensions EEG Transformer (AMDET), which can leverage the complementarity among the spectral-spatial-temporal features of EEG data by employing the multi-dimensional global attention mechanism. We first transform the original EEG data into 3D temporal-spectral-spatial representations and then the AMDET would use spectral-spatial transformer blocks to extract effective features in the EEG signal and focus on the critical time frame with the temporal attention block. We conduct extensive experiments on the DEAP, SEED, and SEED-IV datasets to evaluate the performance of AMDET and the results outperform the state-of-the-art baseline on three datasets. Accuracy rates of 97.48%, 96.85%, 97.17%, 87.32% were achieved in the DEAP-Arousal, DEAP-Valence, SEED, and SEED-IV datasets, respectively. Based on AMDET, we can achieve over 90% accuracy with only eight channels, significantly improving the possibility of practical applications.