{"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/federated-transfer-learning-for-eeg-signal","title":"Federated Transfer Learning for EEG Signal Classification","arxiv_id":"2004.12321","date":"2020-04-26","proceeding":null,"authors":["Ce Ju","Dashan Gao","Ravikiran Mane","Ben Tan","Yang Liu","Cuntai Guan"],"abstract":"The success of deep learning (DL) methods in the Brain-Computer Interfaces (BCI) field for classification of electroencephalographic (EEG) recordings has been restricted by the lack of large datasets. Privacy concerns associated with EEG signals limit the possibility of constructing a large EEG-BCI dataset by the conglomeration of multiple small ones for jointly training machine learning models. Hence, in this paper, we propose a novel privacy-preserving DL architecture named federated transfer learning (FTL) for EEG classification that is based on the federated learning framework. Working with the single-trial covariance matrix, the proposed architecture extracts common discriminative information from multi-subject EEG data with the help of domain adaptation techniques. We evaluate the performance of the proposed architecture on the PhysioNet dataset for 2-class motor imagery classification. While avoiding the actual data sharing, our FTL approach achieves 2% higher classification accuracy in a subject-adaptive analysis. Also, in the absence of multi-subject data, our architecture provides 6% better accuracy compared to other state-of-the-art DL architectures.","url_abs":"https://arxiv.org/abs/2004.12321v5","url_pdf":"https://arxiv.org/pdf/2004.12321v5.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":"federated-transfer-learning-for-eeg-signal","repo_url":"https://github.com/DashanGao/Federated-Transfer-Learning-for-EEG","is_official":1,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"pytorch","reach":null}],"tasks":[{"task_slug":"classification-1","task_name":"Classification"},{"task_slug":"domain-adaptation","task_name":"Domain Adaptation"},{"task_slug":"eeg-1","task_name":"EEG"},{"task_slug":"eeg-signal-classification","task_name":"EEG Signal Classification"},{"task_slug":"eeg","task_name":"Electroencephalogram (EEG)"},{"task_slug":"federated-learning","task_name":"Federated Learning"},{"task_slug":"classification","task_name":"General Classification"},{"task_slug":"motor-imagery","task_name":"Motor Imagery"},{"task_slug":"privacy-preserving","task_name":"Privacy Preserving"},{"task_slug":"transfer-learning","task_name":"Transfer Learning"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"syntology_url":"https://syntology.ai/paper/2004.12321","atlas_url":"https://app.syntology.ai/?focus=2004.12321","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2004.12321"}},"developers":"https://syntology.ai/developers","read_at":"2026-09-25T09:33:49+00:00","read_at_is":"when the build read Syntology's graph, not when any sample ran","claim":"Per-sample execution status on synthesized fixtures; not a correctness claim about the paper. Samples come from repositories linked to the paper, official or community; repo_kind says which.","repos":[{"provenance":"external:paperswithcode_snapshot_2025-07-28","url":"https://github.com/DashanGao/Federated-Transfer-Learning-for-EEG","reach":null}],"summary":{"ran_violates":1,"unverified":1},"by_repo_kind":{"official":{"samples":2,"ran":1,"repositories":1}},"repo_kind_vocabulary":{"official":"The archive marks this repository official for the paper","named_in_paper":"The archive records that the paper mentions this repository; it is not marked official","listed":"In the archive's code links for this paper, not marked official and not recorded as mentioned in the paper","found_in_text":"Syntology found this repository in the paper's own text; whether it is the authors' implementation is not asserted","community":"Not in the archive's code links for this paper; a community repository Syntology harvested"},"n_pointer_only_for_licence":2,"samples":[{"code_sha256_prefix":"40ec25927718f66e","entry":"split_class_feat","repo":"DashanGao/Federated-Transfer-Learning-for-EEG","repo_kind":"official","path":"SPDNet_Federated_Transfer_Learning.py","file_url":"https://github.com/DashanGao/Federated-Transfer-Learning-for-EEG/blob/HEAD/SPDNet_Federated_Transfer_Learning.py","link_basis":"first_harvest_node","language":"python","status":"ran_violates","verification_level":1,"contract_check":"VIOLATES","metamorphic_tier":"invariant","behaviour_fingerprint":true,"licence":"NONE","inline_ok":false,"mcp_get_code":{"code_sha256":"40ec25927718f66e"}},{"code_sha256_prefix":"c347cbd498224467","entry":"load_data","repo":"DashanGao/Federated-Transfer-Learning-for-EEG","repo_kind":"official","path":"SPDNet_Federated_Transfer_Learning.py","file_url":"https://github.com/DashanGao/Federated-Transfer-Learning-for-EEG/blob/HEAD/SPDNet_Federated_Transfer_Learning.py","link_basis":"first_harvest_node","language":"python","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"mcp_get_code":{"code_sha256":"c347cbd498224467"}}]},"arxiv_metadata":null,"syntology_extracted_results":null}