{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2021:THA2EQNEIQS4AJNTBMD5XYVLI5","short_pith_number":"pith:THA2EQNE","schema_version":"1.0","canonical_sha256":"99c1a241a44425c025b30b07dbe2ab47450917a50e77e94c133609807e1d5818","source":{"kind":"arxiv","id":"2112.10894","version":1},"attestation_state":"computed","paper":{"title":"Subject-Independent Drowsiness Recognition from Single-Channel EEG with an Interpretable CNN-LSTM model","license":"http://creativecommons.org/licenses/by-nc-nd/4.0/","headline":"","cross_cats":["cs.LG","eess.SP"],"primary_cat":"cs.NE","authors_text":"Jian Cui, Lipo Wang, Olga Sourina, Tianhu Zheng, Wolfgang M\\\"uller-Wittig, Yisi Liu, Zirui Lan","submitted_at":"2021-11-21T10:37:35Z","abstract_excerpt":"For EEG-based drowsiness recognition, it is desirable to use subject-independent recognition since conducting calibration on each subject is time-consuming. In this paper, we propose a novel Convolutional Neural Network (CNN)-Long Short-Term Memory (LSTM) model for subject-independent drowsiness recognition from single-channel EEG signals. Different from existing deep learning models that are mostly treated as black-box classifiers, the proposed model can explain its decisions for each input sample by revealing which parts of the sample contain important features identified by the model for cl"},"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":"2112.10894","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by-nc-nd/4.0/","primary_cat":"cs.NE","submitted_at":"2021-11-21T10:37:35Z","cross_cats_sorted":["cs.LG","eess.SP"],"title_canon_sha256":"8571e64e2a0743d5a65f87a4175412c142a97b4cea2a7b69ae6467925f44190a","abstract_canon_sha256":"00ce640ce46419c52ad05aebe339e757098cee5d9905b872c41ca01871aab280"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T03:42:27.358505Z","signature_b64":"4XqmQ11zhROvHKb7ETVcC1bzrzVwHe1RjIU4wVWhF0zVLr6aMMPB+nJtM11fh6YJM3dtP4vdKsgKHN3ZD4nZCw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"99c1a241a44425c025b30b07dbe2ab47450917a50e77e94c133609807e1d5818","last_reissued_at":"2026-07-05T03:42:27.358034Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T03:42:27.358034Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Subject-Independent Drowsiness Recognition from Single-Channel EEG with an Interpretable CNN-LSTM model","license":"http://creativecommons.org/licenses/by-nc-nd/4.0/","headline":"","cross_cats":["cs.LG","eess.SP"],"primary_cat":"cs.NE","authors_text":"Jian Cui, Lipo Wang, Olga Sourina, Tianhu Zheng, Wolfgang M\\\"uller-Wittig, Yisi Liu, Zirui Lan","submitted_at":"2021-11-21T10:37:35Z","abstract_excerpt":"For EEG-based drowsiness recognition, it is desirable to use subject-independent recognition since conducting calibration on each subject is time-consuming. In this paper, we propose a novel Convolutional Neural Network (CNN)-Long Short-Term Memory (LSTM) model for subject-independent drowsiness recognition from single-channel EEG signals. Different from existing deep learning models that are mostly treated as black-box classifiers, the proposed model can explain its decisions for each input sample by revealing which parts of the sample contain important features identified by the model for cl"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2112.10894","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/2112.10894/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":"2112.10894","created_at":"2026-07-05T03:42:27.358094+00:00"},{"alias_kind":"arxiv_version","alias_value":"2112.10894v1","created_at":"2026-07-05T03:42:27.358094+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2112.10894","created_at":"2026-07-05T03:42:27.358094+00:00"},{"alias_kind":"pith_short_12","alias_value":"THA2EQNEIQS4","created_at":"2026-07-05T03:42:27.358094+00:00"},{"alias_kind":"pith_short_16","alias_value":"THA2EQNEIQS4AJNT","created_at":"2026-07-05T03:42:27.358094+00:00"},{"alias_kind":"pith_short_8","alias_value":"THA2EQNE","created_at":"2026-07-05T03:42:27.358094+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/THA2EQNEIQS4AJNTBMD5XYVLI5","json":"https://pith.science/pith/THA2EQNEIQS4AJNTBMD5XYVLI5.json","graph_json":"https://pith.science/api/pith-number/THA2EQNEIQS4AJNTBMD5XYVLI5/graph.json","events_json":"https://pith.science/api/pith-number/THA2EQNEIQS4AJNTBMD5XYVLI5/events.json","paper":"https://pith.science/paper/THA2EQNE"},"agent_actions":{"view_html":"https://pith.science/pith/THA2EQNEIQS4AJNTBMD5XYVLI5","download_json":"https://pith.science/pith/THA2EQNEIQS4AJNTBMD5XYVLI5.json","view_paper":"https://pith.science/paper/THA2EQNE","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2112.10894&json=true","fetch_graph":"https://pith.science/api/pith-number/THA2EQNEIQS4AJNTBMD5XYVLI5/graph.json","fetch_events":"https://pith.science/api/pith-number/THA2EQNEIQS4AJNTBMD5XYVLI5/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/THA2EQNEIQS4AJNTBMD5XYVLI5/action/timestamp_anchor","attest_storage":"https://pith.science/pith/THA2EQNEIQS4AJNTBMD5XYVLI5/action/storage_attestation","attest_author":"https://pith.science/pith/THA2EQNEIQS4AJNTBMD5XYVLI5/action/author_attestation","sign_citation":"https://pith.science/pith/THA2EQNEIQS4AJNTBMD5XYVLI5/action/citation_signature","submit_replication":"https://pith.science/pith/THA2EQNEIQS4AJNTBMD5XYVLI5/action/replication_record"}},"created_at":"2026-07-05T03:42:27.358094+00:00","updated_at":"2026-07-05T03:42:27.358094+00:00"}