EEG-ConvTransformer for single-trial EEG-based visual stimulus classification

Abstract

Different categories of visual stimuli evoke distinct activation patterns in the human brain. These patterns can be captured with EEG for utilization in application such as Brain-Computer Interface (BCI). However, accurate classification of these patterns acquired using single-trial data is challenging due to the low signal-to-noise ratio of EEG. Recently, deep learning-based transformer models with multi-head self-attention have shown great potential for analyzing variety of data. This work introduces an EEG-ConvTranformer network that is based on both multi-headed self-attention and temporal convolution. The novel architecture incorporates self-attention modules to capture inter-region interaction patterns and convolutional filters to learn temporal patterns in a single module. Experimental results demonstrate that EEG-ConvTransformer achieves improved classification accuracy over state-of-the-art techniques across five different visual stimulus classification tasks. Finally, quantitative analysis of inter-head diversity also shows low similarity in representational space, emphasizing the implicit diversity of multi-head attention.