{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2018:OB2T4NGIB5O772QO7RN7ISXCCQ","short_pith_number":"pith:OB2T4NGI","schema_version":"1.0","canonical_sha256":"70753e34c80f5dffea0efc5bf44ae21429265f89686ccb3d7e8862571ef3c4dc","source":{"kind":"arxiv","id":"1812.10227","version":2},"attestation_state":"computed","paper":{"title":"Hierarchical feature fusion framework for frequency recognition in SSVEP-based BCIs","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["eess.SP"],"primary_cat":"q-bio.NC","authors_text":"Daqing Guo, Dezhong Yao, Erwei Yin, Fali Li, Peng Xu, Yangsong Zhang, Yu Zhang","submitted_at":"2018-12-26T05:06:16Z","abstract_excerpt":"Effective frequency recognition algorithms are critical in steady-state visual evoked potential (SSVEP) based brain-computer interfaces (BCIs). In this study, we present a hierarchical feature fusion framework which can be used to design high-performance frequency recognition methods. The proposed framework includes two primary technique for fusing features: spatial dimension fusion (SD) and frequency dimension fusion (FD). Both SD and FD fusions are obtained using a weighted strategy with a nonlinear function. To assess our novel methods, we used the correlated component analysis (CORRCA) met"},"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":"1812.10227","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"q-bio.NC","submitted_at":"2018-12-26T05:06:16Z","cross_cats_sorted":["eess.SP"],"title_canon_sha256":"b0bfb2a649282dd18e83dd5623427841f9448d29410d55bf5adf9f1974af5891","abstract_canon_sha256":"96f0798e319143aab69de4bc55bcd7bf4fd17e3fa4bc15998d6ee0a82fbcafa6"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-17T23:50:44.617884Z","signature_b64":"IJ2AdCtWvgsZ51cl+3InDWDw/juuUALN9F1FSFkDOF2hPShd/9jgLOC7tDEQ3Sy0BCbnGvv3VtT29u5tZVOJCQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"70753e34c80f5dffea0efc5bf44ae21429265f89686ccb3d7e8862571ef3c4dc","last_reissued_at":"2026-05-17T23:50:44.617224Z","signature_status":"signed_v1","first_computed_at":"2026-05-17T23:50:44.617224Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Hierarchical feature fusion framework for frequency recognition in SSVEP-based BCIs","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["eess.SP"],"primary_cat":"q-bio.NC","authors_text":"Daqing Guo, Dezhong Yao, Erwei Yin, Fali Li, Peng Xu, Yangsong Zhang, Yu Zhang","submitted_at":"2018-12-26T05:06:16Z","abstract_excerpt":"Effective frequency recognition algorithms are critical in steady-state visual evoked potential (SSVEP) based brain-computer interfaces (BCIs). In this study, we present a hierarchical feature fusion framework which can be used to design high-performance frequency recognition methods. The proposed framework includes two primary technique for fusing features: spatial dimension fusion (SD) and frequency dimension fusion (FD). Both SD and FD fusions are obtained using a weighted strategy with a nonlinear function. To assess our novel methods, we used the correlated component analysis (CORRCA) met"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1812.10227","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":""},"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":"1812.10227","created_at":"2026-05-17T23:50:44.617332+00:00"},{"alias_kind":"arxiv_version","alias_value":"1812.10227v2","created_at":"2026-05-17T23:50:44.617332+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1812.10227","created_at":"2026-05-17T23:50:44.617332+00:00"},{"alias_kind":"pith_short_12","alias_value":"OB2T4NGIB5O7","created_at":"2026-05-18T12:32:43.782077+00:00"},{"alias_kind":"pith_short_16","alias_value":"OB2T4NGIB5O772QO","created_at":"2026-05-18T12:32:43.782077+00:00"},{"alias_kind":"pith_short_8","alias_value":"OB2T4NGI","created_at":"2026-05-18T12:32:43.782077+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/OB2T4NGIB5O772QO7RN7ISXCCQ","json":"https://pith.science/pith/OB2T4NGIB5O772QO7RN7ISXCCQ.json","graph_json":"https://pith.science/api/pith-number/OB2T4NGIB5O772QO7RN7ISXCCQ/graph.json","events_json":"https://pith.science/api/pith-number/OB2T4NGIB5O772QO7RN7ISXCCQ/events.json","paper":"https://pith.science/paper/OB2T4NGI"},"agent_actions":{"view_html":"https://pith.science/pith/OB2T4NGIB5O772QO7RN7ISXCCQ","download_json":"https://pith.science/pith/OB2T4NGIB5O772QO7RN7ISXCCQ.json","view_paper":"https://pith.science/paper/OB2T4NGI","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=1812.10227&json=true","fetch_graph":"https://pith.science/api/pith-number/OB2T4NGIB5O772QO7RN7ISXCCQ/graph.json","fetch_events":"https://pith.science/api/pith-number/OB2T4NGIB5O772QO7RN7ISXCCQ/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/OB2T4NGIB5O772QO7RN7ISXCCQ/action/timestamp_anchor","attest_storage":"https://pith.science/pith/OB2T4NGIB5O772QO7RN7ISXCCQ/action/storage_attestation","attest_author":"https://pith.science/pith/OB2T4NGIB5O772QO7RN7ISXCCQ/action/author_attestation","sign_citation":"https://pith.science/pith/OB2T4NGIB5O772QO7RN7ISXCCQ/action/citation_signature","submit_replication":"https://pith.science/pith/OB2T4NGIB5O772QO7RN7ISXCCQ/action/replication_record"}},"created_at":"2026-05-17T23:50:44.617332+00:00","updated_at":"2026-05-17T23:50:44.617332+00:00"}