{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2025:O6DCTEQX6J3BGNW4PJHCWZY5GT","short_pith_number":"pith:O6DCTEQX","schema_version":"1.0","canonical_sha256":"7786299217f2761336dc7a4e2b671d34f009a6ff3fff8f9b9106b0e43fb5ce2f","source":{"kind":"arxiv","id":"2506.00587","version":1},"attestation_state":"computed","paper":{"title":"Decoding the Stressed Brain with Geometric Machine Learning","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":[],"primary_cat":"cs.LG","authors_text":"Pietro Lio, Sam Nallaperuma-Herzberg, Sonia Koszut","submitted_at":"2025-05-31T14:47:48Z","abstract_excerpt":"Stress significantly contributes to both mental and physical disorders, yet traditional self-reported questionnaires are inherently subjective. In this study, we introduce a novel framework that employs geometric machine learning to detect stress from raw EEG recordings. Our approach constructs graphs by integrating structural connectivity (derived from electrode spatial arrangement) with functional connectivity from pairwise signal correlations. A spatio-temporal graph convolutional network (ST-GCN) processes these graphs to capture spatial and temporal dynamics. Experiments on the SAM-40 dat"},"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":"2506.00587","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2025-05-31T14:47:48Z","cross_cats_sorted":[],"title_canon_sha256":"3b09e8668303ff088a3221a70108fe7e6d60f87bd4b3e6acb9c7bee58ed5649d","abstract_canon_sha256":"0fb422c3b13d75d16e7809aa3311ab66502cd5bbac7944379c4ba0d498d75f3d"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T11:13:37.431102Z","signature_b64":"XL/tmPVIHdZ3BC5MXV/vlYvl98EhE0Puraz/y+TmKNZO3d7f/9GLSpDh9xkgBXZgElwhqwT2M7iYPB+CqX0ZAw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"7786299217f2761336dc7a4e2b671d34f009a6ff3fff8f9b9106b0e43fb5ce2f","last_reissued_at":"2026-07-05T11:13:37.430459Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T11:13:37.430459Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Decoding the Stressed Brain with Geometric Machine Learning","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":[],"primary_cat":"cs.LG","authors_text":"Pietro Lio, Sam Nallaperuma-Herzberg, Sonia Koszut","submitted_at":"2025-05-31T14:47:48Z","abstract_excerpt":"Stress significantly contributes to both mental and physical disorders, yet traditional self-reported questionnaires are inherently subjective. In this study, we introduce a novel framework that employs geometric machine learning to detect stress from raw EEG recordings. Our approach constructs graphs by integrating structural connectivity (derived from electrode spatial arrangement) with functional connectivity from pairwise signal correlations. A spatio-temporal graph convolutional network (ST-GCN) processes these graphs to capture spatial and temporal dynamics. Experiments on the SAM-40 dat"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2506.00587","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/2506.00587/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":"2506.00587","created_at":"2026-07-05T11:13:37.430548+00:00"},{"alias_kind":"arxiv_version","alias_value":"2506.00587v1","created_at":"2026-07-05T11:13:37.430548+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2506.00587","created_at":"2026-07-05T11:13:37.430548+00:00"},{"alias_kind":"pith_short_12","alias_value":"O6DCTEQX6J3B","created_at":"2026-07-05T11:13:37.430548+00:00"},{"alias_kind":"pith_short_16","alias_value":"O6DCTEQX6J3BGNW4","created_at":"2026-07-05T11:13:37.430548+00:00"},{"alias_kind":"pith_short_8","alias_value":"O6DCTEQX","created_at":"2026-07-05T11:13:37.430548+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/O6DCTEQX6J3BGNW4PJHCWZY5GT","json":"https://pith.science/pith/O6DCTEQX6J3BGNW4PJHCWZY5GT.json","graph_json":"https://pith.science/api/pith-number/O6DCTEQX6J3BGNW4PJHCWZY5GT/graph.json","events_json":"https://pith.science/api/pith-number/O6DCTEQX6J3BGNW4PJHCWZY5GT/events.json","paper":"https://pith.science/paper/O6DCTEQX"},"agent_actions":{"view_html":"https://pith.science/pith/O6DCTEQX6J3BGNW4PJHCWZY5GT","download_json":"https://pith.science/pith/O6DCTEQX6J3BGNW4PJHCWZY5GT.json","view_paper":"https://pith.science/paper/O6DCTEQX","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2506.00587&json=true","fetch_graph":"https://pith.science/api/pith-number/O6DCTEQX6J3BGNW4PJHCWZY5GT/graph.json","fetch_events":"https://pith.science/api/pith-number/O6DCTEQX6J3BGNW4PJHCWZY5GT/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/O6DCTEQX6J3BGNW4PJHCWZY5GT/action/timestamp_anchor","attest_storage":"https://pith.science/pith/O6DCTEQX6J3BGNW4PJHCWZY5GT/action/storage_attestation","attest_author":"https://pith.science/pith/O6DCTEQX6J3BGNW4PJHCWZY5GT/action/author_attestation","sign_citation":"https://pith.science/pith/O6DCTEQX6J3BGNW4PJHCWZY5GT/action/citation_signature","submit_replication":"https://pith.science/pith/O6DCTEQX6J3BGNW4PJHCWZY5GT/action/replication_record"}},"created_at":"2026-07-05T11:13:37.430548+00:00","updated_at":"2026-07-05T11:13:37.430548+00:00"}