2021/07/08 by Subhranil Bagchi, Bagchi, Subhranil, Deepti R. Bathula +1
Computer Science · Engineering · Neuroscience · #Advanced Memory and Neural Computing #Computer Vision and Pattern Recognition (cs.CV) #EEG and Brain-Computer Interfaces #FOS: Computer and information sciences #Neural dynamics and brain function #cs.CV
paper · pdf · doi:10.48550/arxiv.2107.03983
Preprint and Supplementary material. 17 pages, 13 figures and 4 tables
arxiv created 2021/07/08 · openalex publication_date 2021/07/08 · arxiv updated 2021/07/09 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
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.