EEG-ConvTransformer for Single-Trial EEG based Visual Stimuli Classification
This work addresses the problem of enhancing Brain-Computer Interface applications by improving EEG-based visual stimuli classification, representing an incremental advancement with a novel hybrid method.
The paper tackles the challenge of accurately classifying single-trial EEG data for visual stimuli classification, which is difficult due to low signal-to-noise ratio, and introduces an EEG-ConvTransformer network that achieves improved classification accuracy over state-of-the-art techniques across five tasks.
Different categories of visual stimuli activate different responses in the human brain. These signals can be captured with EEG for utilization in applications such as Brain-Computer Interface (BCI). However, accurate classification of single-trial data is challenging due to low signal-to-noise ratio of EEG. This work introduces an EEG-ConvTranformer network that is based on multi-headed self-attention. Unlike other transformers, the model incorporates self-attention to capture inter-region interactions. It further extends to adjunct convolutional filters with multi-head attention as a single module to learn temporal patterns. Experimental results demonstrate that EEG-ConvTransformer achieves improved classification accuracy over the state-of-the-art techniques across five different visual stimuli classification tasks. Finally, quantitative analysis of inter-head diversity also shows low similarity in representational subspaces, emphasizing the implicit diversity of multi-head attention.