{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2024:HXC55Q2RPM5OUCH4HDIMCG7UPV","short_pith_number":"pith:HXC55Q2R","schema_version":"1.0","canonical_sha256":"3dc5dec3517b3aea08fc38d0c11bf47d6a189e8e1cfbdec0c7280f0bf4c6f297","source":{"kind":"arxiv","id":"2408.06027","version":2},"attestation_state":"computed","paper":{"title":"A Comprehensive Survey on EEG-Based Emotion Recognition: A Graph-Based Perspective","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.LG"],"primary_cat":"eess.SP","authors_text":"Chenyu Liu, Kun Wang, Liming Zhai, Xinliang Zhou, Yang Liu, Yi Ding, Yihao Wu, Ziyu Jia","submitted_at":"2024-08-12T09:29:26Z","abstract_excerpt":"Compared to other modalities, electroencephalogram (EEG) based emotion recognition can intuitively respond to emotional patterns in the human brain and, therefore, has become one of the most focused tasks in affective computing. The nature of emotions is a physiological and psychological state change in response to brain region connectivity, making emotion recognition focus more on the dependency between brain regions instead of specific brain regions. A significant trend is the application of graphs to encapsulate such dependency as dynamic functional connections between nodes across temporal"},"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":"2408.06027","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"eess.SP","submitted_at":"2024-08-12T09:29:26Z","cross_cats_sorted":["cs.LG"],"title_canon_sha256":"68a3c1f8c98b701e1e87cf87467421500ecd5b84a3f51eb1d1c97876cd933289","abstract_canon_sha256":"72a7fdcc97a028131b0e3e6f02780fbdeb21758c8a744c7a951725da9025594a"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T08:54:55.655413Z","signature_b64":"/1t3zUNks78QwkMQX1NqNYkqok4T+awpXEtoorzbfEzJRUrySIiQS2HQsKwgtrYJBeJE3IYEvjJnGcrCXuxxAA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"3dc5dec3517b3aea08fc38d0c11bf47d6a189e8e1cfbdec0c7280f0bf4c6f297","last_reissued_at":"2026-07-05T08:54:55.655048Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T08:54:55.655048Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"A Comprehensive Survey on EEG-Based Emotion Recognition: A Graph-Based Perspective","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.LG"],"primary_cat":"eess.SP","authors_text":"Chenyu Liu, Kun Wang, Liming Zhai, Xinliang Zhou, Yang Liu, Yi Ding, Yihao Wu, Ziyu Jia","submitted_at":"2024-08-12T09:29:26Z","abstract_excerpt":"Compared to other modalities, electroencephalogram (EEG) based emotion recognition can intuitively respond to emotional patterns in the human brain and, therefore, has become one of the most focused tasks in affective computing. The nature of emotions is a physiological and psychological state change in response to brain region connectivity, making emotion recognition focus more on the dependency between brain regions instead of specific brain regions. A significant trend is the application of graphs to encapsulate such dependency as dynamic functional connections between nodes across temporal"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2408.06027","kind":"arxiv","version":2},"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/2408.06027/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":"2408.06027","created_at":"2026-07-05T08:54:55.655103+00:00"},{"alias_kind":"arxiv_version","alias_value":"2408.06027v2","created_at":"2026-07-05T08:54:55.655103+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2408.06027","created_at":"2026-07-05T08:54:55.655103+00:00"},{"alias_kind":"pith_short_12","alias_value":"HXC55Q2RPM5O","created_at":"2026-07-05T08:54:55.655103+00:00"},{"alias_kind":"pith_short_16","alias_value":"HXC55Q2RPM5OUCH4","created_at":"2026-07-05T08:54:55.655103+00:00"},{"alias_kind":"pith_short_8","alias_value":"HXC55Q2R","created_at":"2026-07-05T08:54:55.655103+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":1,"internal_anchor_count":1,"sample":[{"citing_arxiv_id":"2507.15914","citing_title":"MSGM: A Multi-Scale Spatiotemporal Graph Mamba for EEG Emotion Recognition","ref_index":33,"is_internal_anchor":true}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/HXC55Q2RPM5OUCH4HDIMCG7UPV","json":"https://pith.science/pith/HXC55Q2RPM5OUCH4HDIMCG7UPV.json","graph_json":"https://pith.science/api/pith-number/HXC55Q2RPM5OUCH4HDIMCG7UPV/graph.json","events_json":"https://pith.science/api/pith-number/HXC55Q2RPM5OUCH4HDIMCG7UPV/events.json","paper":"https://pith.science/paper/HXC55Q2R"},"agent_actions":{"view_html":"https://pith.science/pith/HXC55Q2RPM5OUCH4HDIMCG7UPV","download_json":"https://pith.science/pith/HXC55Q2RPM5OUCH4HDIMCG7UPV.json","view_paper":"https://pith.science/paper/HXC55Q2R","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2408.06027&json=true","fetch_graph":"https://pith.science/api/pith-number/HXC55Q2RPM5OUCH4HDIMCG7UPV/graph.json","fetch_events":"https://pith.science/api/pith-number/HXC55Q2RPM5OUCH4HDIMCG7UPV/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/HXC55Q2RPM5OUCH4HDIMCG7UPV/action/timestamp_anchor","attest_storage":"https://pith.science/pith/HXC55Q2RPM5OUCH4HDIMCG7UPV/action/storage_attestation","attest_author":"https://pith.science/pith/HXC55Q2RPM5OUCH4HDIMCG7UPV/action/author_attestation","sign_citation":"https://pith.science/pith/HXC55Q2RPM5OUCH4HDIMCG7UPV/action/citation_signature","submit_replication":"https://pith.science/pith/HXC55Q2RPM5OUCH4HDIMCG7UPV/action/replication_record"}},"created_at":"2026-07-05T08:54:55.655103+00:00","updated_at":"2026-07-05T08:54:55.655103+00:00"}