{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2018:KSQVFPTB2LON7OFHZYTMSKFEXM","short_pith_number":"pith:KSQVFPTB","schema_version":"1.0","canonical_sha256":"54a152be61d2dcdfb8a7ce26c928a4bb27e22c945c1dd7becf3dd06e224c6ced","source":{"kind":"arxiv","id":"1807.10641","version":1},"attestation_state":"computed","paper":{"title":"Multimodal Classification with Deep Convolutional-Recurrent Neural Networks for Electroencephalography","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Chuanqi Tan, Chunfang Liu, Fuchun Sun, Jianhua Chen, Wenchang Zhang","submitted_at":"2018-07-24T03:33:43Z","abstract_excerpt":"Electroencephalography (EEG) has become the most significant input signal for brain computer interface (BCI) based systems. However, it is very difficult to obtain satisfactory classification accuracy due to traditional methods can not fully exploit multimodal information. Herein, we propose a novel approach to modeling cognitive events from EEG data by reducing it to a video classification problem, which is designed to preserve the multimodal information of EEG. In addition, optical flow is introduced to represent the variant information of EEG. We train a deep neural network (DNN) with convo"},"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":"1807.10641","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2018-07-24T03:33:43Z","cross_cats_sorted":[],"title_canon_sha256":"e7392cb0e4b20b897997a4893963e97cb7cc86f9a3339624caa047879eefc892","abstract_canon_sha256":"3e5f4d1b8dffee5fc39789091166a9676299e44b657fdda66efd56bb2989a849"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T00:09:39.917926Z","signature_b64":"Inc919hxanM2fJx2D81wD+X1vWxV5qF2fPp5itZ6iaJpYX/2qCY7ZjvBS+KrdLekQFVIH8cMjP6Rr4jlt719Bg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"54a152be61d2dcdfb8a7ce26c928a4bb27e22c945c1dd7becf3dd06e224c6ced","last_reissued_at":"2026-05-18T00:09:39.917424Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T00:09:39.917424Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Multimodal Classification with Deep Convolutional-Recurrent Neural Networks for Electroencephalography","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Chuanqi Tan, Chunfang Liu, Fuchun Sun, Jianhua Chen, Wenchang Zhang","submitted_at":"2018-07-24T03:33:43Z","abstract_excerpt":"Electroencephalography (EEG) has become the most significant input signal for brain computer interface (BCI) based systems. However, it is very difficult to obtain satisfactory classification accuracy due to traditional methods can not fully exploit multimodal information. Herein, we propose a novel approach to modeling cognitive events from EEG data by reducing it to a video classification problem, which is designed to preserve the multimodal information of EEG. In addition, optical flow is introduced to represent the variant information of EEG. We train a deep neural network (DNN) with convo"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1807.10641","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":""},"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":"1807.10641","created_at":"2026-05-18T00:09:39.917510+00:00"},{"alias_kind":"arxiv_version","alias_value":"1807.10641v1","created_at":"2026-05-18T00:09:39.917510+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1807.10641","created_at":"2026-05-18T00:09:39.917510+00:00"},{"alias_kind":"pith_short_12","alias_value":"KSQVFPTB2LON","created_at":"2026-05-18T12:32:33.847187+00:00"},{"alias_kind":"pith_short_16","alias_value":"KSQVFPTB2LON7OFH","created_at":"2026-05-18T12:32:33.847187+00:00"},{"alias_kind":"pith_short_8","alias_value":"KSQVFPTB","created_at":"2026-05-18T12:32:33.847187+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/KSQVFPTB2LON7OFHZYTMSKFEXM","json":"https://pith.science/pith/KSQVFPTB2LON7OFHZYTMSKFEXM.json","graph_json":"https://pith.science/api/pith-number/KSQVFPTB2LON7OFHZYTMSKFEXM/graph.json","events_json":"https://pith.science/api/pith-number/KSQVFPTB2LON7OFHZYTMSKFEXM/events.json","paper":"https://pith.science/paper/KSQVFPTB"},"agent_actions":{"view_html":"https://pith.science/pith/KSQVFPTB2LON7OFHZYTMSKFEXM","download_json":"https://pith.science/pith/KSQVFPTB2LON7OFHZYTMSKFEXM.json","view_paper":"https://pith.science/paper/KSQVFPTB","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=1807.10641&json=true","fetch_graph":"https://pith.science/api/pith-number/KSQVFPTB2LON7OFHZYTMSKFEXM/graph.json","fetch_events":"https://pith.science/api/pith-number/KSQVFPTB2LON7OFHZYTMSKFEXM/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/KSQVFPTB2LON7OFHZYTMSKFEXM/action/timestamp_anchor","attest_storage":"https://pith.science/pith/KSQVFPTB2LON7OFHZYTMSKFEXM/action/storage_attestation","attest_author":"https://pith.science/pith/KSQVFPTB2LON7OFHZYTMSKFEXM/action/author_attestation","sign_citation":"https://pith.science/pith/KSQVFPTB2LON7OFHZYTMSKFEXM/action/citation_signature","submit_replication":"https://pith.science/pith/KSQVFPTB2LON7OFHZYTMSKFEXM/action/replication_record"}},"created_at":"2026-05-18T00:09:39.917510+00:00","updated_at":"2026-05-18T00:09:39.917510+00:00"}