{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2025:US47RARVTYPLAPYKZBMWECM5GM","merge_version":"pith-open-graph-merge-v1","event_count":2,"valid_event_count":2,"invalid_event_count":0,"equivocation_count":0,"current":{"canonical_record":{"metadata":{"abstract_canon_sha256":"6ffc5370a910f9f37e3ac0d0f39303c88c98451ae240c4216b46d0dae3d70092","cross_cats_sorted":["cs.LG","eess.SP"],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CL","submitted_at":"2025-03-18T11:12:15Z","title_canon_sha256":"216e9432e533eb18a306fecfd6280ba8958db9c47f4eaab67ed67b2a4fa1c68a"},"schema_version":"1.0","source":{"id":"2503.16531","kind":"arxiv","version":2}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2503.16531","created_at":"2026-07-05T11:44:46Z"},{"alias_kind":"arxiv_version","alias_value":"2503.16531v2","created_at":"2026-07-05T11:44:46Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2503.16531","created_at":"2026-07-05T11:44:46Z"},{"alias_kind":"pith_short_12","alias_value":"US47RARVTYPL","created_at":"2026-07-05T11:44:46Z"},{"alias_kind":"pith_short_16","alias_value":"US47RARVTYPLAPYK","created_at":"2026-07-05T11:44:46Z"},{"alias_kind":"pith_short_8","alias_value":"US47RARV","created_at":"2026-07-05T11:44:46Z"}],"graph_snapshots":[{"event_id":"sha256:9577a911b24b6fb21af4b96602ee145ef3c8291c0276ffbb63078181f83e6e66","target":"graph","created_at":"2026-07-05T11:44:46Z","signer":{"key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signer_id":"pith.science","signer_type":"pith_registry"},"payload":{"graph_snapshot":{"author_claims":{"count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57","strong_count":0},"builder_version":"pith-number-builder-2026-05-17-v1","claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"formal_canon":{"evidence_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"integrity":{"available":true,"clean":true,"detectors_run":[],"endpoint":"/pith/2503.16531/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Deep networks for electroencephalogram (EEG) decoding are often only trained to solve one specific task, such as pathology or age decoding. A more general task-agnostic approach is to train deep networks to match a (clinical) EEG recording to its corresponding textual medical report and vice versa. This approach was pioneered in the computer vision domain matching images and their text captions and subsequently allowed to do successful zero-shot decoding using textual class prompts. In this work, we follow this approach and develop a contrastive learning framework, EEG-CLIP, that aligns the EE","authors_text":"Robin Tibor Schirrmeister, Tidiane Camaret Ndir, Tonio Ball","cross_cats":["cs.LG","eess.SP"],"headline":"","license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CL","submitted_at":"2025-03-18T11:12:15Z","title":"EEG-CLIP : Learning EEG representations from natural language descriptions"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2503.16531","kind":"arxiv","version":2},"verdict":{"created_at":null,"id":null,"model_set":{},"one_line_summary":"","pipeline_version":null,"pith_extraction_headline":"","strongest_claim":"","weakest_assumption":""}},"verdict_id":null}}],"author_attestations":[],"timestamp_anchors":[],"storage_attestations":[],"citation_signatures":[],"replication_records":[],"corrections":[],"mirror_hints":[],"record_created":{"event_id":"sha256:68c727f82b307c5b8d0dbd37318624fdaae7660be51aa94c4668c46cb3565c83","target":"record","created_at":"2026-07-05T11:44:46Z","signer":{"key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signer_id":"pith.science","signer_type":"pith_registry"},"payload":{"attestation_state":"computed","canonical_record":{"metadata":{"abstract_canon_sha256":"6ffc5370a910f9f37e3ac0d0f39303c88c98451ae240c4216b46d0dae3d70092","cross_cats_sorted":["cs.LG","eess.SP"],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CL","submitted_at":"2025-03-18T11:12:15Z","title_canon_sha256":"216e9432e533eb18a306fecfd6280ba8958db9c47f4eaab67ed67b2a4fa1c68a"},"schema_version":"1.0","source":{"id":"2503.16531","kind":"arxiv","version":2}},"canonical_sha256":"a4b9f882359e1eb03f0ac85962099d3308a898224a7b66f06a82b6ab48ead5fc","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"a4b9f882359e1eb03f0ac85962099d3308a898224a7b66f06a82b6ab48ead5fc","first_computed_at":"2026-07-05T11:44:46.817628Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T11:44:46.817628Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"BEiLoxqIkF/bbyAv25gKYBJmHE02smA8xc580JpkCGE3/ZCTmS/0wbDuVR/EpfKLwlS0r+bauUnZQmd0NZgUDQ==","signature_status":"signed_v1","signed_at":"2026-07-05T11:44:46.818088Z","signed_message":"canonical_sha256_bytes"},"source_id":"2503.16531","source_kind":"arxiv","source_version":2}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:68c727f82b307c5b8d0dbd37318624fdaae7660be51aa94c4668c46cb3565c83","sha256:9577a911b24b6fb21af4b96602ee145ef3c8291c0276ffbb63078181f83e6e66"],"state_sha256":"03649c78da375c00a9a95dfb7e82465b553e9296fa3974a3849cf0a933dea3ae"}