EEG-based Emotion Recognition via Transformer Neural Architecture Search

Abstract

Emotion recognition based on electroencephalogram (EEG) plays an increasingly important role in the field of brain–computer interfaces. Recently, deep learning has been widely applied to EEG decoding owning to its excellent capabilities in automatic feature extraction. Transformer holds great superiority in processing time-series signals due to its long-term dependencies extraction ability. However, most existing transformer architectures are designed manually by human experts, which is a time-consuming and resource-intensive process. In this article, we propose an automatic transformer neural architectures search (TNAS) framework based on multiobjective evolution algorithm (MOEA) for the EEG-based emotion recognition. The proposed TNAS conducts the MOEA strategy that considers both accuracy and model size to discover the optimal model from well-trained supernet for the emotion recognition. We conducted extensive experiments to evaluate the performance of the proposed TNAS on the DEAP and DREAMER datasets. The experimental results showed that the proposed TNAS outperforms the state-of-the-art methods.