{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2024:DUWLSMBCD3HCXGLJZCET5XZHLZ","short_pith_number":"pith:DUWLSMBC","schema_version":"1.0","canonical_sha256":"1d2cb930221ece2b9969c8893edf275e511d0ee0f508056424d53a5beac0bc1a","source":{"kind":"arxiv","id":"2401.10278","version":1},"attestation_state":"computed","paper":{"title":"EEGFormer: Towards Transferable and Interpretable Large-Scale EEG Foundation Model","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.AI","cs.LG","cs.MM","q-bio.NC"],"primary_cat":"eess.SP","authors_text":"Dongsheng Li, Kaitao Song, Kan Ren, Lili Qiu, Yansen Wang, Yifan Wang, Yuqi Chen","submitted_at":"2024-01-11T17:36:24Z","abstract_excerpt":"Self-supervised learning has emerged as a highly effective approach in the fields of natural language processing and computer vision. It is also applicable to brain signals such as electroencephalography (EEG) data, given the abundance of available unlabeled data that exist in a wide spectrum of real-world medical applications ranging from seizure detection to wave analysis. The existing works leveraging self-supervised learning on EEG modeling mainly focus on pretraining upon each individual dataset corresponding to a single downstream task, which cannot leverage the power of abundant data, a"},"verification_status":{"content_addressed":true,"pith_receipt":true,"author_attested":false,"weak_author_claims":0,"strong_author_claims":0,"externally_anchored":false,"storage_verified":false,"citation_signatures":0,"replication_records":0,"graph_snapshot":true,"references_resolved":false,"formal_links_present":false},"canonical_record":{"source":{"id":"2401.10278","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"eess.SP","submitted_at":"2024-01-11T17:36:24Z","cross_cats_sorted":["cs.AI","cs.LG","cs.MM","q-bio.NC"],"title_canon_sha256":"68de763b23ac987779f4dfbcd3a27cebdf7a557759b3510481fc453a19497427","abstract_canon_sha256":"62939309760d59e79edd595ded4063994998b2321b5f3f8ece3f338e7771de09"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T07:35:09.583260Z","signature_b64":"fOH9SV4SJJuIHgXf2pVptjy0BcaNt7ldIDNadTn2KDvtVhBYQ9q1nhKGudXZQdDBO5L1Zqj/Mes6+lrh5prnCQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"1d2cb930221ece2b9969c8893edf275e511d0ee0f508056424d53a5beac0bc1a","last_reissued_at":"2026-07-05T07:35:09.582769Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T07:35:09.582769Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"EEGFormer: Towards Transferable and Interpretable Large-Scale EEG Foundation Model","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.AI","cs.LG","cs.MM","q-bio.NC"],"primary_cat":"eess.SP","authors_text":"Dongsheng Li, Kaitao Song, Kan Ren, Lili Qiu, Yansen Wang, Yifan Wang, Yuqi Chen","submitted_at":"2024-01-11T17:36:24Z","abstract_excerpt":"Self-supervised learning has emerged as a highly effective approach in the fields of natural language processing and computer vision. It is also applicable to brain signals such as electroencephalography (EEG) data, given the abundance of available unlabeled data that exist in a wide spectrum of real-world medical applications ranging from seizure detection to wave analysis. The existing works leveraging self-supervised learning on EEG modeling mainly focus on pretraining upon each individual dataset corresponding to a single downstream task, which cannot leverage the power of abundant data, a"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2401.10278","kind":"arxiv","version":1},"verdict":{"id":null,"model_set":{},"created_at":null,"strongest_claim":"","one_line_summary":"","pipeline_version":null,"weakest_assumption":"","pith_extraction_headline":""},"integrity":{"clean":true,"summary":{"advisory":0,"critical":0,"by_detector":{},"informational":0},"endpoint":"/pith/2401.10278/integrity.json","findings":[],"available":true,"detectors_run":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938"},"references":{"count":0,"sample":[],"resolved_work":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57","internal_anchors":0},"formal_canon":{"evidence_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"author_claims":{"count":0,"strong_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"builder_version":"pith-number-builder-2026-05-17-v1"},"aliases":[{"alias_kind":"arxiv","alias_value":"2401.10278","created_at":"2026-07-05T07:35:09.582833+00:00"},{"alias_kind":"arxiv_version","alias_value":"2401.10278v1","created_at":"2026-07-05T07:35:09.582833+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2401.10278","created_at":"2026-07-05T07:35:09.582833+00:00"},{"alias_kind":"pith_short_12","alias_value":"DUWLSMBCD3HC","created_at":"2026-07-05T07:35:09.582833+00:00"},{"alias_kind":"pith_short_16","alias_value":"DUWLSMBCD3HCXGLJ","created_at":"2026-07-05T07:35:09.582833+00:00"},{"alias_kind":"pith_short_8","alias_value":"DUWLSMBC","created_at":"2026-07-05T07:35:09.582833+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":8,"internal_anchor_count":0,"sample":[{"citing_arxiv_id":"2606.24087","citing_title":"NeuroSonic: Conditional Flow Matching for EEG-to-Speech Reconstruction","ref_index":5,"is_internal_anchor":false},{"citing_arxiv_id":"2606.01767","citing_title":"EvoBrain: Continual Learning of EEG Foundation Models Across Heterogeneous BCI Tasks","ref_index":50,"is_internal_anchor":false},{"citing_arxiv_id":"2606.30319","citing_title":"BrainJanus: A Unified Model for Understanding and Generation across Brain, Vision, and Language","ref_index":6,"is_internal_anchor":false},{"citing_arxiv_id":"2502.17873","citing_title":"An Efficient Self-Supervised Framework for Long-Sequence EEG Modeling","ref_index":11,"is_internal_anchor":false},{"citing_arxiv_id":"2605.21280","citing_title":"Let EEG Models Learn EEG","ref_index":10,"is_internal_anchor":false},{"citing_arxiv_id":"2605.09579","citing_title":"Biosignal Fingerprinting: A Cross-Modal PPG-ECG Foundation Model","ref_index":13,"is_internal_anchor":false},{"citing_arxiv_id":"2604.18827","citing_title":"OmniMouse: Scaling properties of multi-modal, multi-task Brain Models on 150B Neural Tokens","ref_index":51,"is_internal_anchor":false},{"citing_arxiv_id":"2605.02917","citing_title":"PRISM-CTG: A Foundation Model for Cardiotocography Analysis with Multi-View SSL","ref_index":5,"is_internal_anchor":false}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/DUWLSMBCD3HCXGLJZCET5XZHLZ","json":"https://pith.science/pith/DUWLSMBCD3HCXGLJZCET5XZHLZ.json","graph_json":"https://pith.science/api/pith-number/DUWLSMBCD3HCXGLJZCET5XZHLZ/graph.json","events_json":"https://pith.science/api/pith-number/DUWLSMBCD3HCXGLJZCET5XZHLZ/events.json","paper":"https://pith.science/paper/DUWLSMBC"},"agent_actions":{"view_html":"https://pith.science/pith/DUWLSMBCD3HCXGLJZCET5XZHLZ","download_json":"https://pith.science/pith/DUWLSMBCD3HCXGLJZCET5XZHLZ.json","view_paper":"https://pith.science/paper/DUWLSMBC","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2401.10278&json=true","fetch_graph":"https://pith.science/api/pith-number/DUWLSMBCD3HCXGLJZCET5XZHLZ/graph.json","fetch_events":"https://pith.science/api/pith-number/DUWLSMBCD3HCXGLJZCET5XZHLZ/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/DUWLSMBCD3HCXGLJZCET5XZHLZ/action/timestamp_anchor","attest_storage":"https://pith.science/pith/DUWLSMBCD3HCXGLJZCET5XZHLZ/action/storage_attestation","attest_author":"https://pith.science/pith/DUWLSMBCD3HCXGLJZCET5XZHLZ/action/author_attestation","sign_citation":"https://pith.science/pith/DUWLSMBCD3HCXGLJZCET5XZHLZ/action/citation_signature","submit_replication":"https://pith.science/pith/DUWLSMBCD3HCXGLJZCET5XZHLZ/action/replication_record"}},"created_at":"2026-07-05T07:35:09.582833+00:00","updated_at":"2026-07-05T07:35:09.582833+00:00"}