{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/eeg-convtransformer-for-single-trial-eeg","title":"EEG-ConvTransformer for Single-Trial EEG based Visual Stimuli Classification","arxiv_id":"2107.03983","date":"2021-07-08","proceeding":null,"authors":["Subhranil Bagchi","Deepti R. Bathula"],"abstract":"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.","url_abs":"https://arxiv.org/abs/2107.03983v1","url_pdf":"https://arxiv.org/pdf/2107.03983v1.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[{"paper_slug":"eeg-convtransformer-for-single-trial-eeg","repo_url":"https://github.com/MeetXinZhang/EEG-ConvTransformer","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":0,"framework":"pytorch","reach":null}],"tasks":[{"task_slug":"brain-computer-interface","task_name":"Brain Computer Interface"},{"task_slug":"classification-1","task_name":"Classification"},{"task_slug":"diversity","task_name":"Diversity"},{"task_slug":"eeg-1","task_name":"EEG"},{"task_slug":"eeg","task_name":"Electroencephalogram (EEG)"}],"methods":[{"method_slug":"linear-layer","method_name":"Linear Layer"},{"method_slug":"softmax","method_name":"Softmax"}],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}