{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2025:2N4TY25UWN44NHDBI2A36WAAE7","short_pith_number":"pith:2N4TY25U","schema_version":"1.0","canonical_sha256":"d3793c6bb4b379c69c614681bf580027e568b7d398b022175e246fd4064c23ed","source":{"kind":"arxiv","id":"2503.13475","version":1},"attestation_state":"computed","paper":{"title":"Cross-Subject Depression Level Classification Using EEG Signals with a Sample Confidence Method","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.AI","cs.LG"],"primary_cat":"eess.SP","authors_text":"Chenyang Xu, Huirang Hou, Lixuan Zhao, Qinghao Meng, Zhongyi Zhang","submitted_at":"2025-03-04T13:16:11Z","abstract_excerpt":"Electroencephalogram (EEG) is a non-invasive tool for real-time neural monitoring,widely used in depression detection via deep learning. However, existing models primarily focus on binary classification (depression/normal), lacking granularity for severity assessment. To address this, we proposed the DepL-GCN, i.e., Depression Level classification based on GCN model. This model tackles two key challenges: (1) subjectivity in depres-sion-level labeling due to patient self-report biases, and (2) class imbalance across severity categories. Inspired by the model learning patterns, we introduced tw"},"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":"2503.13475","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"eess.SP","submitted_at":"2025-03-04T13:16:11Z","cross_cats_sorted":["cs.AI","cs.LG"],"title_canon_sha256":"b8d3c235724a3b8828b9af8ab6ec1e5e6cd10a05bc4e9898087bea964fd3bfd0","abstract_canon_sha256":"56a430b3ee0fd06f8249513f2e8228d538192579e3d77abd3b89d47c30ec62b6"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T10:33:10.717526Z","signature_b64":"sJEB25byzVkn8AgmlDAkpGdQ9kEqlWcTa+SFp8X2Yuq4J6n7Oae9XUv2BZDW6wcj4PiVthkpja/fE+XffwxkAw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"d3793c6bb4b379c69c614681bf580027e568b7d398b022175e246fd4064c23ed","last_reissued_at":"2026-07-05T10:33:10.717084Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T10:33:10.717084Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Cross-Subject Depression Level Classification Using EEG Signals with a Sample Confidence Method","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.AI","cs.LG"],"primary_cat":"eess.SP","authors_text":"Chenyang Xu, Huirang Hou, Lixuan Zhao, Qinghao Meng, Zhongyi Zhang","submitted_at":"2025-03-04T13:16:11Z","abstract_excerpt":"Electroencephalogram (EEG) is a non-invasive tool for real-time neural monitoring,widely used in depression detection via deep learning. However, existing models primarily focus on binary classification (depression/normal), lacking granularity for severity assessment. To address this, we proposed the DepL-GCN, i.e., Depression Level classification based on GCN model. This model tackles two key challenges: (1) subjectivity in depres-sion-level labeling due to patient self-report biases, and (2) class imbalance across severity categories. Inspired by the model learning patterns, we introduced tw"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2503.13475","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/2503.13475/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":"2503.13475","created_at":"2026-07-05T10:33:10.717147+00:00"},{"alias_kind":"arxiv_version","alias_value":"2503.13475v1","created_at":"2026-07-05T10:33:10.717147+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2503.13475","created_at":"2026-07-05T10:33:10.717147+00:00"},{"alias_kind":"pith_short_12","alias_value":"2N4TY25UWN44","created_at":"2026-07-05T10:33:10.717147+00:00"},{"alias_kind":"pith_short_16","alias_value":"2N4TY25UWN44NHDB","created_at":"2026-07-05T10:33:10.717147+00:00"},{"alias_kind":"pith_short_8","alias_value":"2N4TY25U","created_at":"2026-07-05T10:33:10.717147+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/2N4TY25UWN44NHDBI2A36WAAE7","json":"https://pith.science/pith/2N4TY25UWN44NHDBI2A36WAAE7.json","graph_json":"https://pith.science/api/pith-number/2N4TY25UWN44NHDBI2A36WAAE7/graph.json","events_json":"https://pith.science/api/pith-number/2N4TY25UWN44NHDBI2A36WAAE7/events.json","paper":"https://pith.science/paper/2N4TY25U"},"agent_actions":{"view_html":"https://pith.science/pith/2N4TY25UWN44NHDBI2A36WAAE7","download_json":"https://pith.science/pith/2N4TY25UWN44NHDBI2A36WAAE7.json","view_paper":"https://pith.science/paper/2N4TY25U","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2503.13475&json=true","fetch_graph":"https://pith.science/api/pith-number/2N4TY25UWN44NHDBI2A36WAAE7/graph.json","fetch_events":"https://pith.science/api/pith-number/2N4TY25UWN44NHDBI2A36WAAE7/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/2N4TY25UWN44NHDBI2A36WAAE7/action/timestamp_anchor","attest_storage":"https://pith.science/pith/2N4TY25UWN44NHDBI2A36WAAE7/action/storage_attestation","attest_author":"https://pith.science/pith/2N4TY25UWN44NHDBI2A36WAAE7/action/author_attestation","sign_citation":"https://pith.science/pith/2N4TY25UWN44NHDBI2A36WAAE7/action/citation_signature","submit_replication":"https://pith.science/pith/2N4TY25UWN44NHDBI2A36WAAE7/action/replication_record"}},"created_at":"2026-07-05T10:33:10.717147+00:00","updated_at":"2026-07-05T10:33:10.717147+00:00"}