{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2024:XYKYDWUJ4X2ACJCDJMBHMGS2KG","short_pith_number":"pith:XYKYDWUJ","schema_version":"1.0","canonical_sha256":"be1581da89e5f40124434b02761a5a5193735c588f4af4881da4818ba2c93a98","source":{"kind":"arxiv","id":"2409.07589","version":2},"attestation_state":"computed","paper":{"title":"miMamba: EEG-based Emotion Recognition with Multi-scale Inverted Mamba Models","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.LG","eess.SP"],"primary_cat":"cs.HC","authors_text":"Dawei Huang, Lijun Yin, Xiaojing Peng, Xin Zhou","submitted_at":"2024-09-11T19:39:58Z","abstract_excerpt":"EEG-based emotion recognition holds significant potential in the field of brain-computer interfaces. A key challenge lies in extracting discriminative spatiotemporal features from electroencephalogram (EEG) signals. Existing studies often rely on domain-specific time-frequency features and analyze temporal dependencies and spatial characteristics separately, neglecting the interaction between local-global relationships and spatiotemporal dynamics. To address this, we propose a novel network called Multi-Scale Inverted Mamba (MS-iMamba), which consists of Multi-Scale Temporal Blocks (MSTB) and "},"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":"2409.07589","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.HC","submitted_at":"2024-09-11T19:39:58Z","cross_cats_sorted":["cs.LG","eess.SP"],"title_canon_sha256":"1cd2621fdcf5f520765b44bd48674f955be316d4df720e7876d86876b175d892","abstract_canon_sha256":"9c265d525683e0ca08ba46aec24da140bcdc38507fd5c0272af9e4911ddfcb15"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-07T02:18:22.781142Z","signature_b64":"9DyEaOwV2MqnINvMFaZX9W6Ghx4S8E62dSa+zXPNMOIBrE7rIYzNC7Ascvk84B7MDhvRXRWCJMIB+eJmjPRxDQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"be1581da89e5f40124434b02761a5a5193735c588f4af4881da4818ba2c93a98","last_reissued_at":"2026-07-07T02:18:22.777533Z","signature_status":"signed_v1","first_computed_at":"2026-07-07T02:18:22.777533Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"miMamba: EEG-based Emotion Recognition with Multi-scale Inverted Mamba Models","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.LG","eess.SP"],"primary_cat":"cs.HC","authors_text":"Dawei Huang, Lijun Yin, Xiaojing Peng, Xin Zhou","submitted_at":"2024-09-11T19:39:58Z","abstract_excerpt":"EEG-based emotion recognition holds significant potential in the field of brain-computer interfaces. A key challenge lies in extracting discriminative spatiotemporal features from electroencephalogram (EEG) signals. Existing studies often rely on domain-specific time-frequency features and analyze temporal dependencies and spatial characteristics separately, neglecting the interaction between local-global relationships and spatiotemporal dynamics. To address this, we propose a novel network called Multi-Scale Inverted Mamba (MS-iMamba), which consists of Multi-Scale Temporal Blocks (MSTB) and "},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2409.07589","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/2409.07589/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":"2409.07589","created_at":"2026-07-07T02:18:22.777645+00:00"},{"alias_kind":"arxiv_version","alias_value":"2409.07589v2","created_at":"2026-07-07T02:18:22.777645+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2409.07589","created_at":"2026-07-07T02:18:22.777645+00:00"},{"alias_kind":"pith_short_12","alias_value":"XYKYDWUJ4X2A","created_at":"2026-07-07T02:18:22.777645+00:00"},{"alias_kind":"pith_short_16","alias_value":"XYKYDWUJ4X2ACJCD","created_at":"2026-07-07T02:18:22.777645+00:00"},{"alias_kind":"pith_short_8","alias_value":"XYKYDWUJ","created_at":"2026-07-07T02:18:22.777645+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/XYKYDWUJ4X2ACJCDJMBHMGS2KG","json":"https://pith.science/pith/XYKYDWUJ4X2ACJCDJMBHMGS2KG.json","graph_json":"https://pith.science/api/pith-number/XYKYDWUJ4X2ACJCDJMBHMGS2KG/graph.json","events_json":"https://pith.science/api/pith-number/XYKYDWUJ4X2ACJCDJMBHMGS2KG/events.json","paper":"https://pith.science/paper/XYKYDWUJ"},"agent_actions":{"view_html":"https://pith.science/pith/XYKYDWUJ4X2ACJCDJMBHMGS2KG","download_json":"https://pith.science/pith/XYKYDWUJ4X2ACJCDJMBHMGS2KG.json","view_paper":"https://pith.science/paper/XYKYDWUJ","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2409.07589&json=true","fetch_graph":"https://pith.science/api/pith-number/XYKYDWUJ4X2ACJCDJMBHMGS2KG/graph.json","fetch_events":"https://pith.science/api/pith-number/XYKYDWUJ4X2ACJCDJMBHMGS2KG/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/XYKYDWUJ4X2ACJCDJMBHMGS2KG/action/timestamp_anchor","attest_storage":"https://pith.science/pith/XYKYDWUJ4X2ACJCDJMBHMGS2KG/action/storage_attestation","attest_author":"https://pith.science/pith/XYKYDWUJ4X2ACJCDJMBHMGS2KG/action/author_attestation","sign_citation":"https://pith.science/pith/XYKYDWUJ4X2ACJCDJMBHMGS2KG/action/citation_signature","submit_replication":"https://pith.science/pith/XYKYDWUJ4X2ACJCDJMBHMGS2KG/action/replication_record"}},"created_at":"2026-07-07T02:18:22.777645+00:00","updated_at":"2026-07-07T02:18:22.777645+00:00"}