{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2022:Z65FEMU7PPGCWBBTNBXL4K3HR6","short_pith_number":"pith:Z65FEMU7","schema_version":"1.0","canonical_sha256":"cfba52329f7bcc2b0433686ebe2b678fa8c5b6e1e828ca6b1a2267659a8033d0","source":{"kind":"arxiv","id":"2208.00883","version":1},"attestation_state":"computed","paper":{"title":"A Two-Stage Efficient 3-D CNN Framework for EEG Based Emotion Recognition","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.AI","cs.HC","cs.LG"],"primary_cat":"eess.SP","authors_text":"Mohammed Alnemari, Nader Bagherzadeh, Ye Qiao","submitted_at":"2022-07-26T05:33:08Z","abstract_excerpt":"This paper proposes a novel two-stage framework for emotion recognition using EEG data that outperforms state-of-the-art models while keeping the model size small and computationally efficient. The framework consists of two stages; the first stage involves constructing efficient models named EEGNet, which is inspired by the state-of-the-art efficient architecture and employs inverted-residual blocks that contain depthwise separable convolutional layers. The EEGNet models on both valence and arousal labels achieve the average classification accuracy of 90%, 96.6%, and 99.5% with only 6.4k, 14k,"},"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":"2208.00883","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"eess.SP","submitted_at":"2022-07-26T05:33:08Z","cross_cats_sorted":["cs.AI","cs.HC","cs.LG"],"title_canon_sha256":"b8c829d62f90d9a8b01e57bc383f0e5d2f159e2a2bfd33d53bc2e820da3d3473","abstract_canon_sha256":"d533702ee2a261300a7c5d43b14da4f1ba5304e87952bf0cf9ca2f8b51a105e8"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T04:45:06.000039Z","signature_b64":"ppK80cQFuhqVRlzPKS7I/7kMDn0NPVesLIRYgNmzrvRdt55c3H/7FRxxKjdXBy36d5/0W92diEviOCuNV40QAQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"cfba52329f7bcc2b0433686ebe2b678fa8c5b6e1e828ca6b1a2267659a8033d0","last_reissued_at":"2026-07-05T04:45:05.999632Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T04:45:05.999632Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"A Two-Stage Efficient 3-D CNN Framework for EEG Based Emotion Recognition","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.AI","cs.HC","cs.LG"],"primary_cat":"eess.SP","authors_text":"Mohammed Alnemari, Nader Bagherzadeh, Ye Qiao","submitted_at":"2022-07-26T05:33:08Z","abstract_excerpt":"This paper proposes a novel two-stage framework for emotion recognition using EEG data that outperforms state-of-the-art models while keeping the model size small and computationally efficient. The framework consists of two stages; the first stage involves constructing efficient models named EEGNet, which is inspired by the state-of-the-art efficient architecture and employs inverted-residual blocks that contain depthwise separable convolutional layers. The EEGNet models on both valence and arousal labels achieve the average classification accuracy of 90%, 96.6%, and 99.5% with only 6.4k, 14k,"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2208.00883","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/2208.00883/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":"2208.00883","created_at":"2026-07-05T04:45:05.999690+00:00"},{"alias_kind":"arxiv_version","alias_value":"2208.00883v1","created_at":"2026-07-05T04:45:05.999690+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2208.00883","created_at":"2026-07-05T04:45:05.999690+00:00"},{"alias_kind":"pith_short_12","alias_value":"Z65FEMU7PPGC","created_at":"2026-07-05T04:45:05.999690+00:00"},{"alias_kind":"pith_short_16","alias_value":"Z65FEMU7PPGCWBBT","created_at":"2026-07-05T04:45:05.999690+00:00"},{"alias_kind":"pith_short_8","alias_value":"Z65FEMU7","created_at":"2026-07-05T04:45:05.999690+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":21,"is_internal_anchor":true}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/Z65FEMU7PPGCWBBTNBXL4K3HR6","json":"https://pith.science/pith/Z65FEMU7PPGCWBBTNBXL4K3HR6.json","graph_json":"https://pith.science/api/pith-number/Z65FEMU7PPGCWBBTNBXL4K3HR6/graph.json","events_json":"https://pith.science/api/pith-number/Z65FEMU7PPGCWBBTNBXL4K3HR6/events.json","paper":"https://pith.science/paper/Z65FEMU7"},"agent_actions":{"view_html":"https://pith.science/pith/Z65FEMU7PPGCWBBTNBXL4K3HR6","download_json":"https://pith.science/pith/Z65FEMU7PPGCWBBTNBXL4K3HR6.json","view_paper":"https://pith.science/paper/Z65FEMU7","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2208.00883&json=true","fetch_graph":"https://pith.science/api/pith-number/Z65FEMU7PPGCWBBTNBXL4K3HR6/graph.json","fetch_events":"https://pith.science/api/pith-number/Z65FEMU7PPGCWBBTNBXL4K3HR6/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/Z65FEMU7PPGCWBBTNBXL4K3HR6/action/timestamp_anchor","attest_storage":"https://pith.science/pith/Z65FEMU7PPGCWBBTNBXL4K3HR6/action/storage_attestation","attest_author":"https://pith.science/pith/Z65FEMU7PPGCWBBTNBXL4K3HR6/action/author_attestation","sign_citation":"https://pith.science/pith/Z65FEMU7PPGCWBBTNBXL4K3HR6/action/citation_signature","submit_replication":"https://pith.science/pith/Z65FEMU7PPGCWBBTNBXL4K3HR6/action/replication_record"}},"created_at":"2026-07-05T04:45:05.999690+00:00","updated_at":"2026-07-05T04:45:05.999690+00:00"}