{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2020:WXRQOUVWWS6H35FOAORB7GHG2C","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":"3a6f8119e95a340cf82ac283a605d296a5be9129861855054e9f89ab1d2be6d6","cross_cats_sorted":["cs.LG","eess.SP","stat.ML"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"eess.IV","submitted_at":"2020-02-11T17:12:24Z","title_canon_sha256":"d918b8f77947def2b9ab3bd65332da9f062bac96c49aad240234ac21e304c39b"},"schema_version":"1.0","source":{"id":"2002.05115","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2002.05115","created_at":"2026-07-05T02:18:14Z"},{"alias_kind":"arxiv_version","alias_value":"2002.05115v1","created_at":"2026-07-05T02:18:14Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2002.05115","created_at":"2026-07-05T02:18:14Z"},{"alias_kind":"pith_short_12","alias_value":"WXRQOUVWWS6H","created_at":"2026-07-05T02:18:14Z"},{"alias_kind":"pith_short_16","alias_value":"WXRQOUVWWS6H35FO","created_at":"2026-07-05T02:18:14Z"},{"alias_kind":"pith_short_8","alias_value":"WXRQOUVW","created_at":"2026-07-05T02:18:14Z"}],"graph_snapshots":[{"event_id":"sha256:87e7c52a453f4d6c5cc8c822ab74af70a9e0f1e4ac3c21a77f41fe2fc9459ca9","target":"graph","created_at":"2026-07-05T02:18:14Z","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/2002.05115/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Machine learning (ML) methods have the potential to automate clinical EEG analysis. They can be categorized into feature-based (with handcrafted features), and end-to-end approaches (with learned features). Previous studies on EEG pathology decoding have typically analyzed a limited number of features, decoders, or both. For a I) more elaborate feature-based EEG analysis, and II) in-depth comparisons of both approaches, here we first develop a comprehensive feature-based framework, and then compare this framework to state-of-the-art end-to-end methods. To this aim, we apply the proposed featur","authors_text":"Andreas Schulze-Bonhage, Daniel Wilson, Frank Hutter, Joschka Boedecker, Lukas Alexander Wilhelm Gemein, Patryk Chrab\\k{a}szcz, Robin Tibor Schirrmeister, Tonio Ball","cross_cats":["cs.LG","eess.SP","stat.ML"],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"eess.IV","submitted_at":"2020-02-11T17:12:24Z","title":"Machine-Learning-Based Diagnostics of EEG Pathology"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2002.05115","kind":"arxiv","version":1},"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":"sha256:4d83c27069783ba9c01a8b6d368ed506ea5f232e5929ca31f1107f65371986c3","target":"record","created_at":"2026-07-05T02:18:14Z","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":"3a6f8119e95a340cf82ac283a605d296a5be9129861855054e9f89ab1d2be6d6","cross_cats_sorted":["cs.LG","eess.SP","stat.ML"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"eess.IV","submitted_at":"2020-02-11T17:12:24Z","title_canon_sha256":"d918b8f77947def2b9ab3bd65332da9f062bac96c49aad240234ac21e304c39b"},"schema_version":"1.0","source":{"id":"2002.05115","kind":"arxiv","version":1}},"canonical_sha256":"b5e30752b6b4bc7df4ae03a21f98e6d0b2152e39c4cfb6eddc042b2561e3d6d6","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"b5e30752b6b4bc7df4ae03a21f98e6d0b2152e39c4cfb6eddc042b2561e3d6d6","first_computed_at":"2026-07-05T02:18:14.912977Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T02:18:14.912977Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"OmYsZmUhH5YY0YCPq0TdiGMjPs1pkY2ifC1sIJEL5AT82l6Tl9f6lDv4BhIBjfSxoLANigVwLhK3U2UXk+ptAg==","signature_status":"signed_v1","signed_at":"2026-07-05T02:18:14.913460Z","signed_message":"canonical_sha256_bytes"},"source_id":"2002.05115","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:4d83c27069783ba9c01a8b6d368ed506ea5f232e5929ca31f1107f65371986c3","sha256:87e7c52a453f4d6c5cc8c822ab74af70a9e0f1e4ac3c21a77f41fe2fc9459ca9"],"state_sha256":"3ec43d4eb2b877b765e0f30933566b24eae96beec34e8e65fca18c778ee5ea08"}