{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2024:SLCRFOUW52XCB6U7U7XXAIEOKD","short_pith_number":"pith:SLCRFOUW","schema_version":"1.0","canonical_sha256":"92c512ba96eeae20fa9fa7ef70208e50d8a2d239e82a87d99728dd40e0951275","source":{"kind":"arxiv","id":"2412.19487","version":1},"attestation_state":"computed","paper":{"title":"UniBrain: A Unified Model for Cross-Subject Brain Decoding","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Luping Zhou, Parashkev Nachev, Zhen Zhao, Zicheng Wang","submitted_at":"2024-12-27T07:03:47Z","abstract_excerpt":"Brain decoding aims to reconstruct original stimuli from fMRI signals, providing insights into interpreting mental content. Current approaches rely heavily on subject-specific models due to the complex brain processing mechanisms and the variations in fMRI signals across individuals. Therefore, these methods greatly limit the generalization of models and fail to capture cross-subject commonalities. To address this, we present UniBrain, a unified brain decoding model that requires no subject-specific parameters. Our approach includes a group-based extractor to handle variable fMRI signal length"},"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":"2412.19487","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2024-12-27T07:03:47Z","cross_cats_sorted":[],"title_canon_sha256":"6219b3c9b533880ef9e6594eff94cf2754362e885645befdfc0d08c72f6b880c","abstract_canon_sha256":"c69ae2edbf529fd584b6e6441e57d6b9b04fa6345446b4b4ef06fd2149deba4c"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T09:54:36.500982Z","signature_b64":"1JrARKcfmc3H+NdmLQwQrglQw1pPpLDvdvbqnqh0xVnjCEtcVxebnHmyWs2vp2liAXLCdrBoZjzgk9NVaPfADA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"92c512ba96eeae20fa9fa7ef70208e50d8a2d239e82a87d99728dd40e0951275","last_reissued_at":"2026-07-05T09:54:36.500583Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T09:54:36.500583Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"UniBrain: A Unified Model for Cross-Subject Brain Decoding","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Luping Zhou, Parashkev Nachev, Zhen Zhao, Zicheng Wang","submitted_at":"2024-12-27T07:03:47Z","abstract_excerpt":"Brain decoding aims to reconstruct original stimuli from fMRI signals, providing insights into interpreting mental content. Current approaches rely heavily on subject-specific models due to the complex brain processing mechanisms and the variations in fMRI signals across individuals. Therefore, these methods greatly limit the generalization of models and fail to capture cross-subject commonalities. To address this, we present UniBrain, a unified brain decoding model that requires no subject-specific parameters. Our approach includes a group-based extractor to handle variable fMRI signal length"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2412.19487","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/2412.19487/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":"2412.19487","created_at":"2026-07-05T09:54:36.500638+00:00"},{"alias_kind":"arxiv_version","alias_value":"2412.19487v1","created_at":"2026-07-05T09:54:36.500638+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2412.19487","created_at":"2026-07-05T09:54:36.500638+00:00"},{"alias_kind":"pith_short_12","alias_value":"SLCRFOUW52XC","created_at":"2026-07-05T09:54:36.500638+00:00"},{"alias_kind":"pith_short_16","alias_value":"SLCRFOUW52XCB6U7","created_at":"2026-07-05T09:54:36.500638+00:00"},{"alias_kind":"pith_short_8","alias_value":"SLCRFOUW","created_at":"2026-07-05T09:54:36.500638+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":4,"internal_anchor_count":0,"sample":[{"citing_arxiv_id":"2605.29588","citing_title":"Brain-IT-VQA: From Brain Signals to Answers","ref_index":17,"is_internal_anchor":false},{"citing_arxiv_id":"2605.19279","citing_title":"FPED: A Functional-Network Prior-Guided Mixture-of-Experts Framework for Interpretable Brain Decoding","ref_index":8,"is_internal_anchor":false},{"citing_arxiv_id":"2512.20249","citing_title":"Unified Multimodal Brain Decoding via Cross-Subject Soft-ROI Fusion","ref_index":10,"is_internal_anchor":false},{"citing_arxiv_id":"2605.02586","citing_title":"StableMind: Source-Free Cross-Subject fMRI Decoding with Regularized Adaptation","ref_index":28,"is_internal_anchor":false}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/SLCRFOUW52XCB6U7U7XXAIEOKD","json":"https://pith.science/pith/SLCRFOUW52XCB6U7U7XXAIEOKD.json","graph_json":"https://pith.science/api/pith-number/SLCRFOUW52XCB6U7U7XXAIEOKD/graph.json","events_json":"https://pith.science/api/pith-number/SLCRFOUW52XCB6U7U7XXAIEOKD/events.json","paper":"https://pith.science/paper/SLCRFOUW"},"agent_actions":{"view_html":"https://pith.science/pith/SLCRFOUW52XCB6U7U7XXAIEOKD","download_json":"https://pith.science/pith/SLCRFOUW52XCB6U7U7XXAIEOKD.json","view_paper":"https://pith.science/paper/SLCRFOUW","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2412.19487&json=true","fetch_graph":"https://pith.science/api/pith-number/SLCRFOUW52XCB6U7U7XXAIEOKD/graph.json","fetch_events":"https://pith.science/api/pith-number/SLCRFOUW52XCB6U7U7XXAIEOKD/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/SLCRFOUW52XCB6U7U7XXAIEOKD/action/timestamp_anchor","attest_storage":"https://pith.science/pith/SLCRFOUW52XCB6U7U7XXAIEOKD/action/storage_attestation","attest_author":"https://pith.science/pith/SLCRFOUW52XCB6U7U7XXAIEOKD/action/author_attestation","sign_citation":"https://pith.science/pith/SLCRFOUW52XCB6U7U7XXAIEOKD/action/citation_signature","submit_replication":"https://pith.science/pith/SLCRFOUW52XCB6U7U7XXAIEOKD/action/replication_record"}},"created_at":"2026-07-05T09:54:36.500638+00:00","updated_at":"2026-07-05T09:54:36.500638+00:00"}