{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2023:ES2MH76JYA5XIJIHJ6JT5X7UFE","short_pith_number":"pith:ES2MH76J","schema_version":"1.0","canonical_sha256":"24b4c3ffc9c03b7425074f933edff42920daada08a742c234ef66adc16790547","source":{"kind":"arxiv","id":"2310.19812","version":3},"attestation_state":"computed","paper":{"title":"Brain decoding: toward real-time reconstruction of visual perception","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.AI","cs.LG","q-bio.NC"],"primary_cat":"eess.IV","authors_text":"Hubert Banville, Jean-R\\'emi King, Yohann Benchetrit","submitted_at":"2023-10-18T09:51:38Z","abstract_excerpt":"In the past five years, the use of generative and foundational AI systems has greatly improved the decoding of brain activity. Visual perception, in particular, can now be decoded from functional Magnetic Resonance Imaging (fMRI) with remarkable fidelity. This neuroimaging technique, however, suffers from a limited temporal resolution ($\\approx$0.5 Hz) and thus fundamentally constrains its real-time usage. Here, we propose an alternative approach based on magnetoencephalography (MEG), a neuroimaging device capable of measuring brain activity with high temporal resolution ($\\approx$5,000 Hz). F"},"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":"2310.19812","kind":"arxiv","version":3},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"eess.IV","submitted_at":"2023-10-18T09:51:38Z","cross_cats_sorted":["cs.AI","cs.LG","q-bio.NC"],"title_canon_sha256":"391001e845a4cd22f0ab5ab61f7f5041f75b891a72ece3f23a0166ffe1360dc9","abstract_canon_sha256":"7d9aff8172b85aa8c7c9a8cb0d8982bd270b5788836ee379b15cc7e9e5d7161b"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T07:56:00.862761Z","signature_b64":"uZJMH5e7F6D86Y30tDVFXryk5x90AENi0XGpg7TIzWrFAkZLMhuQIuDwHPLlD3bqpIPx4f1MVCc5w6Uyvk/VCQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"24b4c3ffc9c03b7425074f933edff42920daada08a742c234ef66adc16790547","last_reissued_at":"2026-07-05T07:56:00.862070Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T07:56:00.862070Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Brain decoding: toward real-time reconstruction of visual perception","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.AI","cs.LG","q-bio.NC"],"primary_cat":"eess.IV","authors_text":"Hubert Banville, Jean-R\\'emi King, Yohann Benchetrit","submitted_at":"2023-10-18T09:51:38Z","abstract_excerpt":"In the past five years, the use of generative and foundational AI systems has greatly improved the decoding of brain activity. Visual perception, in particular, can now be decoded from functional Magnetic Resonance Imaging (fMRI) with remarkable fidelity. This neuroimaging technique, however, suffers from a limited temporal resolution ($\\approx$0.5 Hz) and thus fundamentally constrains its real-time usage. Here, we propose an alternative approach based on magnetoencephalography (MEG), a neuroimaging device capable of measuring brain activity with high temporal resolution ($\\approx$5,000 Hz). F"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2310.19812","kind":"arxiv","version":3},"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/2310.19812/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":"2310.19812","created_at":"2026-07-05T07:56:00.862142+00:00"},{"alias_kind":"arxiv_version","alias_value":"2310.19812v3","created_at":"2026-07-05T07:56:00.862142+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2310.19812","created_at":"2026-07-05T07:56:00.862142+00:00"},{"alias_kind":"pith_short_12","alias_value":"ES2MH76JYA5X","created_at":"2026-07-05T07:56:00.862142+00:00"},{"alias_kind":"pith_short_16","alias_value":"ES2MH76JYA5XIJIH","created_at":"2026-07-05T07:56:00.862142+00:00"},{"alias_kind":"pith_short_8","alias_value":"ES2MH76J","created_at":"2026-07-05T07:56:00.862142+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":8,"internal_anchor_count":0,"sample":[{"citing_arxiv_id":"2605.24523","citing_title":"MindAlign: Bridging EEG, Vision, and Language for Zero-Shot Visual Decoding","ref_index":31,"is_internal_anchor":false},{"citing_arxiv_id":"2605.14569","citing_title":"Bridging Brain and Semantics: A Hierarchical Framework for Semantically Enhanced fMRI-to-Video Reconstruction","ref_index":4,"is_internal_anchor":false},{"citing_arxiv_id":"2605.04326","citing_title":"A foundation model of vision, audition, and language for in-silico neuroscience","ref_index":12,"is_internal_anchor":false},{"citing_arxiv_id":"2605.04680","citing_title":"Multi-Level Bidirectional Biomimetic Learning for EEG-Based Visual Decoding","ref_index":6,"is_internal_anchor":false},{"citing_arxiv_id":"2605.00662","citing_title":"Spiking Sequence Machines and Transformers","ref_index":4,"is_internal_anchor":false},{"citing_arxiv_id":"2604.18827","citing_title":"OmniMouse: Scaling properties of multi-modal, multi-task Brain Models on 150B Neural Tokens","ref_index":83,"is_internal_anchor":false},{"citing_arxiv_id":"2604.09817","citing_title":"NeuroFlow: Toward Unified Visual Encoding and Decoding from Neural Activity","ref_index":5,"is_internal_anchor":false},{"citing_arxiv_id":"2604.08537","citing_title":"Meta-learning In-Context Enables Training-Free Cross Subject Brain Decoding","ref_index":8,"is_internal_anchor":false}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/ES2MH76JYA5XIJIHJ6JT5X7UFE","json":"https://pith.science/pith/ES2MH76JYA5XIJIHJ6JT5X7UFE.json","graph_json":"https://pith.science/api/pith-number/ES2MH76JYA5XIJIHJ6JT5X7UFE/graph.json","events_json":"https://pith.science/api/pith-number/ES2MH76JYA5XIJIHJ6JT5X7UFE/events.json","paper":"https://pith.science/paper/ES2MH76J"},"agent_actions":{"view_html":"https://pith.science/pith/ES2MH76JYA5XIJIHJ6JT5X7UFE","download_json":"https://pith.science/pith/ES2MH76JYA5XIJIHJ6JT5X7UFE.json","view_paper":"https://pith.science/paper/ES2MH76J","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2310.19812&json=true","fetch_graph":"https://pith.science/api/pith-number/ES2MH76JYA5XIJIHJ6JT5X7UFE/graph.json","fetch_events":"https://pith.science/api/pith-number/ES2MH76JYA5XIJIHJ6JT5X7UFE/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/ES2MH76JYA5XIJIHJ6JT5X7UFE/action/timestamp_anchor","attest_storage":"https://pith.science/pith/ES2MH76JYA5XIJIHJ6JT5X7UFE/action/storage_attestation","attest_author":"https://pith.science/pith/ES2MH76JYA5XIJIHJ6JT5X7UFE/action/author_attestation","sign_citation":"https://pith.science/pith/ES2MH76JYA5XIJIHJ6JT5X7UFE/action/citation_signature","submit_replication":"https://pith.science/pith/ES2MH76JYA5XIJIHJ6JT5X7UFE/action/replication_record"}},"created_at":"2026-07-05T07:56:00.862142+00:00","updated_at":"2026-07-05T07:56:00.862142+00:00"}