{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2026:F6YTX6ALANPPHHLKHHSGWSJ464","short_pith_number":"pith:F6YTX6AL","schema_version":"1.0","canonical_sha256":"2fb13bf80b035ef39d6a39e46b493cf70ad4f10eac8bd2899f1a4edd7b88cc4d","source":{"kind":"arxiv","id":"2605.17562","version":1},"attestation_state":"computed","paper":{"title":"Beyond Accuracy: Robustness, Interpretability and Expressiveness of EEG Foundation Models","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.AI","cs.HC"],"primary_cat":"cs.LG","authors_text":"Konstantinos Barmpas, Maryam Alimardani, Stefanos Zafeiriou, Urban \\v{S}irca","submitted_at":"2026-05-17T17:47:00Z","abstract_excerpt":"EEG foundation models (EEG-FMs) have been evaluated predominantly on clean, in-distribution accuracy, leaving their robustness, interpretability and representational quality largely unexamined. This study addresses these gaps by benchmarking six EEG-FMs against a baseline deep learning model across eight datasets. Beyond clean accuracy, we conduct three layers of analysis: (i) Robustness: we apply test-time perturbations including additive noise, random and region-based channel dropout and region-specific noise injection. Our analyses show that no single model dominates all failure modes. The "},"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":"2605.17562","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2026-05-17T17:47:00Z","cross_cats_sorted":["cs.AI","cs.HC"],"title_canon_sha256":"3007de5ed06b41d2e1c06827410fbf1dfc7b9383f6d5d0b596757831b392bbb5","abstract_canon_sha256":"f0c486fcacaa1c5736423dcc847b4940031ad3a0cc328c0d02c64411e2398b58"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-20T00:04:46.060031Z","signature_b64":"6myukCJMrnA0bUnX2dA01mCZJZ4x0Tr0clApdcJj4pEZjW6UQaPFsiHWZvcMNBuiKto01yBeG9KwF6xCGC/RAw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"2fb13bf80b035ef39d6a39e46b493cf70ad4f10eac8bd2899f1a4edd7b88cc4d","last_reissued_at":"2026-05-20T00:04:46.059159Z","signature_status":"signed_v1","first_computed_at":"2026-05-20T00:04:46.059159Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Beyond Accuracy: Robustness, Interpretability and Expressiveness of EEG Foundation Models","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.AI","cs.HC"],"primary_cat":"cs.LG","authors_text":"Konstantinos Barmpas, Maryam Alimardani, Stefanos Zafeiriou, Urban \\v{S}irca","submitted_at":"2026-05-17T17:47:00Z","abstract_excerpt":"EEG foundation models (EEG-FMs) have been evaluated predominantly on clean, in-distribution accuracy, leaving their robustness, interpretability and representational quality largely unexamined. This study addresses these gaps by benchmarking six EEG-FMs against a baseline deep learning model across eight datasets. Beyond clean accuracy, we conduct three layers of analysis: (i) Robustness: we apply test-time perturbations including additive noise, random and region-based channel dropout and region-specific noise injection. Our analyses show that no single model dominates all failure modes. The "},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2605.17562","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/2605.17562/integrity.json","findings":[],"available":true,"detectors_run":[{"name":"ai_meta_artifact","ran_at":"2026-05-19T21:33:23.600499Z","status":"skipped","version":"1.0.0","findings_count":0},{"name":"claim_evidence","ran_at":"2026-05-19T21:21:57.532991Z","status":"completed","version":"1.0.0","findings_count":0}],"snapshot_sha256":"8a1002aa7b2a9b5b99311b3cd3d059f75f5916356f450c029a125bde8457077b"},"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":"2605.17562","created_at":"2026-05-20T00:04:46.059309+00:00"},{"alias_kind":"arxiv_version","alias_value":"2605.17562v1","created_at":"2026-05-20T00:04:46.059309+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2605.17562","created_at":"2026-05-20T00:04:46.059309+00:00"},{"alias_kind":"pith_short_12","alias_value":"F6YTX6ALANPP","created_at":"2026-05-20T00:04:46.059309+00:00"},{"alias_kind":"pith_short_16","alias_value":"F6YTX6ALANPPHHLK","created_at":"2026-05-20T00:04:46.059309+00:00"},{"alias_kind":"pith_short_8","alias_value":"F6YTX6AL","created_at":"2026-05-20T00:04:46.059309+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":0,"internal_anchor_count":0,"sample":[]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/F6YTX6ALANPPHHLKHHSGWSJ464","json":"https://pith.science/pith/F6YTX6ALANPPHHLKHHSGWSJ464.json","graph_json":"https://pith.science/api/pith-number/F6YTX6ALANPPHHLKHHSGWSJ464/graph.json","events_json":"https://pith.science/api/pith-number/F6YTX6ALANPPHHLKHHSGWSJ464/events.json","paper":"https://pith.science/paper/F6YTX6AL"},"agent_actions":{"view_html":"https://pith.science/pith/F6YTX6ALANPPHHLKHHSGWSJ464","download_json":"https://pith.science/pith/F6YTX6ALANPPHHLKHHSGWSJ464.json","view_paper":"https://pith.science/paper/F6YTX6AL","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2605.17562&json=true","fetch_graph":"https://pith.science/api/pith-number/F6YTX6ALANPPHHLKHHSGWSJ464/graph.json","fetch_events":"https://pith.science/api/pith-number/F6YTX6ALANPPHHLKHHSGWSJ464/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/F6YTX6ALANPPHHLKHHSGWSJ464/action/timestamp_anchor","attest_storage":"https://pith.science/pith/F6YTX6ALANPPHHLKHHSGWSJ464/action/storage_attestation","attest_author":"https://pith.science/pith/F6YTX6ALANPPHHLKHHSGWSJ464/action/author_attestation","sign_citation":"https://pith.science/pith/F6YTX6ALANPPHHLKHHSGWSJ464/action/citation_signature","submit_replication":"https://pith.science/pith/F6YTX6ALANPPHHLKHHSGWSJ464/action/replication_record"}},"created_at":"2026-05-20T00:04:46.059309+00:00","updated_at":"2026-05-20T00:04:46.059309+00:00"}