{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2025:PKTTEFUB2CFDHNWQ4QQNZDO7PC","short_pith_number":"pith:PKTTEFUB","schema_version":"1.0","canonical_sha256":"7aa7321681d08a33b6d0e420dc8ddf78b1f2753d825b4a81a9f489172298c824","source":{"kind":"arxiv","id":"2505.12408","version":3},"attestation_state":"computed","paper":{"title":"ViEEG: Hierarchical Visual Neural Representation for EEG Brain Decoding","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.AI","cs.HC"],"primary_cat":"cs.CV","authors_text":"Chuhang Zheng, Chunwei Tian, Donghai Guan, Jie Wen, Minxu Liu, Qi Zhu","submitted_at":"2025-05-18T13:19:08Z","abstract_excerpt":"Understanding and decoding brain activity into visual representations is a fundamental challenge at the intersection of neuroscience and artificial intelligence. While EEG visual decoding has shown promise due to its non-invasive, and low-cost nature, existing methods suffer from Hierarchical Neural Encoding Neglect (HNEN)-a critical limitation where flat neural representations fail to model the brain's hierarchical visual processing hierarchy. Inspired by the hierarchical organization of visual cortex, we propose ViEEG, a neuro-We further adopt hierarchical contrastive learning for EEG-CLIP r"},"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":"2505.12408","kind":"arxiv","version":3},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2025-05-18T13:19:08Z","cross_cats_sorted":["cs.AI","cs.HC"],"title_canon_sha256":"955232dd909d6898b665fbfbb9eb45fc6b48a219fc2eff55df64daec0b7cd84a","abstract_canon_sha256":"d5441811cea185aa5e208e74859b4a118dc4d83fd3c29aaf07d7f7d76828fbbf"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T12:02:46.328227Z","signature_b64":"sfiL5gqSUxfZyjSySQq+ejvCjNejseaalqGFnxzn+beHhlFY9qEXvnAxsIcQUlXDUd7J2w9AX3j7Dk+HzFy4CA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"7aa7321681d08a33b6d0e420dc8ddf78b1f2753d825b4a81a9f489172298c824","last_reissued_at":"2026-07-05T12:02:46.327696Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T12:02:46.327696Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"ViEEG: Hierarchical Visual Neural Representation for EEG Brain Decoding","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.AI","cs.HC"],"primary_cat":"cs.CV","authors_text":"Chuhang Zheng, Chunwei Tian, Donghai Guan, Jie Wen, Minxu Liu, Qi Zhu","submitted_at":"2025-05-18T13:19:08Z","abstract_excerpt":"Understanding and decoding brain activity into visual representations is a fundamental challenge at the intersection of neuroscience and artificial intelligence. While EEG visual decoding has shown promise due to its non-invasive, and low-cost nature, existing methods suffer from Hierarchical Neural Encoding Neglect (HNEN)-a critical limitation where flat neural representations fail to model the brain's hierarchical visual processing hierarchy. Inspired by the hierarchical organization of visual cortex, we propose ViEEG, a neuro-We further adopt hierarchical contrastive learning for EEG-CLIP r"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2505.12408","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/2505.12408/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":"2505.12408","created_at":"2026-07-05T12:02:46.327765+00:00"},{"alias_kind":"arxiv_version","alias_value":"2505.12408v3","created_at":"2026-07-05T12:02:46.327765+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2505.12408","created_at":"2026-07-05T12:02:46.327765+00:00"},{"alias_kind":"pith_short_12","alias_value":"PKTTEFUB2CFD","created_at":"2026-07-05T12:02:46.327765+00:00"},{"alias_kind":"pith_short_16","alias_value":"PKTTEFUB2CFDHNWQ","created_at":"2026-07-05T12:02:46.327765+00:00"},{"alias_kind":"pith_short_8","alias_value":"PKTTEFUB","created_at":"2026-07-05T12:02:46.327765+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":2,"internal_anchor_count":0,"sample":[{"citing_arxiv_id":"2605.04680","citing_title":"Multi-Level Bidirectional Biomimetic Learning for EEG-Based Visual Decoding","ref_index":19,"is_internal_anchor":false},{"citing_arxiv_id":"2604.17927","citing_title":"Brain-Inspired Capture: Evidence-Driven Neuromimetic Perceptual Simulation for Visual Decoding","ref_index":29,"is_internal_anchor":false}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/PKTTEFUB2CFDHNWQ4QQNZDO7PC","json":"https://pith.science/pith/PKTTEFUB2CFDHNWQ4QQNZDO7PC.json","graph_json":"https://pith.science/api/pith-number/PKTTEFUB2CFDHNWQ4QQNZDO7PC/graph.json","events_json":"https://pith.science/api/pith-number/PKTTEFUB2CFDHNWQ4QQNZDO7PC/events.json","paper":"https://pith.science/paper/PKTTEFUB"},"agent_actions":{"view_html":"https://pith.science/pith/PKTTEFUB2CFDHNWQ4QQNZDO7PC","download_json":"https://pith.science/pith/PKTTEFUB2CFDHNWQ4QQNZDO7PC.json","view_paper":"https://pith.science/paper/PKTTEFUB","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2505.12408&json=true","fetch_graph":"https://pith.science/api/pith-number/PKTTEFUB2CFDHNWQ4QQNZDO7PC/graph.json","fetch_events":"https://pith.science/api/pith-number/PKTTEFUB2CFDHNWQ4QQNZDO7PC/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/PKTTEFUB2CFDHNWQ4QQNZDO7PC/action/timestamp_anchor","attest_storage":"https://pith.science/pith/PKTTEFUB2CFDHNWQ4QQNZDO7PC/action/storage_attestation","attest_author":"https://pith.science/pith/PKTTEFUB2CFDHNWQ4QQNZDO7PC/action/author_attestation","sign_citation":"https://pith.science/pith/PKTTEFUB2CFDHNWQ4QQNZDO7PC/action/citation_signature","submit_replication":"https://pith.science/pith/PKTTEFUB2CFDHNWQ4QQNZDO7PC/action/replication_record"}},"created_at":"2026-07-05T12:02:46.327765+00:00","updated_at":"2026-07-05T12:02:46.327765+00:00"}