{"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/transfer-learning-for-brain-computer","title":"Transfer Learning for Brain-Computer Interfaces: A Euclidean Space Data Alignment Approach","arxiv_id":"1808.05464","date":"2018-08-08","proceeding":null,"authors":["He He","Dongrui Wu"],"abstract":"Objective: This paper targets a major challenge in developing practical\nEEG-based brain-computer interfaces (BCIs): how to cope with individual\ndifferences so that better learning performance can be obtained for a new\nsubject, with minimum or even no subject-specific data? Methods: We propose a\nnovel approach to align EEG trials from different subjects in the Euclidean\nspace to make them more similar, and hence improve the learning performance for\na new subject. Our approach has three desirable properties: 1) it aligns the\nEEG trials directly in the Euclidean space, and any signal processing, feature\nextraction and machine learning algorithms can then be applied to the aligned\ntrials; 2) its computational cost is very low; and, 3) it is unsupervised and\ndoes not need any label information from the new subject. Results: Both offline\nand simulated online experiments on motor imagery classification and\nevent-related potential classification verified that our proposed approach\noutperformed a state-of-the-art Riemannian space data alignment approach, and\nseveral approaches without data alignment. Conclusion: The proposed Euclidean\nspace EEG data alignment approach can greatly facilitate transfer learning in\nBCIs. Significance: Our proposed approach is effective, efficient, and easy to\nimplement. It could be an essential pre-processing step for EEG-based BCIs.","url_abs":"http://arxiv.org/abs/1808.05464v2","url_pdf":"http://arxiv.org/pdf/1808.05464v2.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":"transfer-learning-for-brain-computer","repo_url":"https://github.com/mcd4874/neurips_competition","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"pytorch","reach":null}],"tasks":[{"task_slug":"eeg-1","task_name":"EEG"},{"task_slug":"eeg","task_name":"Electroencephalogram (EEG)"},{"task_slug":"classification","task_name":"General Classification"},{"task_slug":"motor-imagery","task_name":"Motor Imagery"},{"task_slug":"transfer-learning","task_name":"Transfer Learning"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=1808.05464","mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}