{"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/domain-invariant-representation-learning-from","title":"Domain-Invariant Representation Learning from EEG with Private Encoders","arxiv_id":"2201.11613","date":"2022-01-27","proceeding":null,"authors":["David Bethge","Philipp Hallgarten","Tobias Grosse-Puppendahl","Mohamed Kari","Ralf Mikut","Albrecht Schmidt","Ozan Özdenizci"],"abstract":"Deep learning based electroencephalography (EEG) signal processing methods are known to suffer from poor test-time generalization due to the changes in data distribution. This becomes a more challenging problem when privacy-preserving representation learning is of interest such as in clinical settings. To that end, we propose a multi-source learning architecture where we extract domain-invariant representations from dataset-specific private encoders. Our model utilizes a maximum-mean-discrepancy (MMD) based domain alignment approach to impose domain-invariance for encoded representations, which outperforms state-of-the-art approaches in EEG-based emotion classification. Furthermore, representations learned in our pipeline preserve domain privacy as dataset-specific private encoding alleviates the need for conventional, centralized EEG-based deep neural network training approaches with shared parameters.","url_abs":"https://arxiv.org/abs/2201.11613v2","url_pdf":"https://arxiv.org/pdf/2201.11613v2.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":"domain-invariant-representation-learning-from","repo_url":"https://github.com/philipph77/DAPE-Framework","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":0,"framework":"pytorch","reach":null}],"tasks":[{"task_slug":"eeg-1","task_name":"EEG"},{"task_slug":"eeg","task_name":"Electroencephalogram (EEG)"},{"task_slug":"emotion-classification","task_name":"Emotion Classification"},{"task_slug":"privacy-preserving","task_name":"Privacy Preserving"},{"task_slug":"representation-learning","task_name":"Representation Learning"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=2201.11613","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2201.11613"}},"developers":"https://syntology.ai/developers","read_at":"2026-09-24T18:15:14+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/philipph77/DAPE-Framework","reach":null}],"summary":{"unverified":3},"by_repo_kind":{"official":{"samples":3,"ran":0,"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":0,"samples":[{"code_sha256_prefix":"bf7a0ac9040a85fb","entry":"test","repo":"philipph77/DAPE-Framework","repo_kind":"official","path":"pipeline_funcs.py","file_url":"https://github.com/philipph77/DAPE-Framework/blob/HEAD/pipeline_funcs.py","link_basis":"first_harvest_node","language":"python","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"mcp_get_code":{"code_sha256":"bf7a0ac9040a85fb"}},{"code_sha256_prefix":"ea1b1f0817902456","entry":"test_adversarial","repo":"philipph77/DAPE-Framework","repo_kind":"official","path":"pipeline_funcs.py","file_url":"https://github.com/philipph77/DAPE-Framework/blob/HEAD/pipeline_funcs.py","link_basis":"first_harvest_node","language":"python","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"mcp_get_code":{"code_sha256":"ea1b1f0817902456"}},{"code_sha256_prefix":"71241a71df217bff","entry":"train_adversarial","repo":"philipph77/DAPE-Framework","repo_kind":"official","path":"pipeline_funcs.py","file_url":"https://github.com/philipph77/DAPE-Framework/blob/HEAD/pipeline_funcs.py","link_basis":"first_harvest_node","language":"python","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"mcp_get_code":{"code_sha256":"71241a71df217bff"}}]},"arxiv_metadata":null,"syntology_extracted_results":null}