{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2026:GUBZLR7I7JL7LIHEP2OLWJ7VB6","short_pith_number":"pith:GUBZLR7I","schema_version":"1.0","canonical_sha256":"350395c7e8fa57f5a0e47e9cbb27f50fa54e455e26a5ac3e94a1cd6e3b8b948a","source":{"kind":"arxiv","id":"2607.04139","version":1},"attestation_state":"computed","paper":{"title":"Masked Generative-Contrastive Representation Learning for Cross-Dataset EEG-Based Emotion Recognition","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.LG","authors_text":"Chi-Man Vong, Chuangquan Chen, Huqin Weng, Jiayang Huang, Jie Du, Yimin Wen","submitted_at":"2026-07-05T06:44:17Z","abstract_excerpt":"Self-supervised learning (SSL) shows strong potential for cross-dataset transfer by improving feature representation and generalization. However, its application to EEG-based emotion recognition remains largely unexplored. Existing SSL methods struggle to capture the intricate spatiotemporal dependencies of EEG signals under varying channel configurations, extract fine-grained representations resilient to noise, and derive global features that generalize well across subjects. To address these challenges, we propose Masked Generative-Contrastive Representation Learning (MGCRL), a novel SSL fram"},"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":"2607.04139","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2026-07-05T06:44:17Z","cross_cats_sorted":[],"title_canon_sha256":"b03bc4cbf608581c8c7a48ad26c3625363404de4ecb7cc66c248a03936671a20","abstract_canon_sha256":"a9cd267ec77cebe2d15c07cf516479f8e1a330f2feceedce10d30ae9902116eb"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-07T02:18:59.255002Z","signature_b64":"mogneCTjJDuhBMGfU2cPJFYzOKhUnPJofb+YsTEsgd1bQO6T5362jbahDcA1wCtRj/FK7m8THCdYU1tvMJXdAg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"350395c7e8fa57f5a0e47e9cbb27f50fa54e455e26a5ac3e94a1cd6e3b8b948a","last_reissued_at":"2026-07-07T02:18:59.254187Z","signature_status":"signed_v1","first_computed_at":"2026-07-07T02:18:59.254187Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Masked Generative-Contrastive Representation Learning for Cross-Dataset EEG-Based Emotion Recognition","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.LG","authors_text":"Chi-Man Vong, Chuangquan Chen, Huqin Weng, Jiayang Huang, Jie Du, Yimin Wen","submitted_at":"2026-07-05T06:44:17Z","abstract_excerpt":"Self-supervised learning (SSL) shows strong potential for cross-dataset transfer by improving feature representation and generalization. However, its application to EEG-based emotion recognition remains largely unexplored. Existing SSL methods struggle to capture the intricate spatiotemporal dependencies of EEG signals under varying channel configurations, extract fine-grained representations resilient to noise, and derive global features that generalize well across subjects. To address these challenges, we propose Masked Generative-Contrastive Representation Learning (MGCRL), a novel SSL fram"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2607.04139","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/2607.04139/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":"2607.04139","created_at":"2026-07-07T02:18:59.254300+00:00"},{"alias_kind":"arxiv_version","alias_value":"2607.04139v1","created_at":"2026-07-07T02:18:59.254300+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2607.04139","created_at":"2026-07-07T02:18:59.254300+00:00"},{"alias_kind":"pith_short_12","alias_value":"GUBZLR7I7JL7","created_at":"2026-07-07T02:18:59.254300+00:00"},{"alias_kind":"pith_short_16","alias_value":"GUBZLR7I7JL7LIHE","created_at":"2026-07-07T02:18:59.254300+00:00"},{"alias_kind":"pith_short_8","alias_value":"GUBZLR7I","created_at":"2026-07-07T02:18:59.254300+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/GUBZLR7I7JL7LIHEP2OLWJ7VB6","json":"https://pith.science/pith/GUBZLR7I7JL7LIHEP2OLWJ7VB6.json","graph_json":"https://pith.science/api/pith-number/GUBZLR7I7JL7LIHEP2OLWJ7VB6/graph.json","events_json":"https://pith.science/api/pith-number/GUBZLR7I7JL7LIHEP2OLWJ7VB6/events.json","paper":"https://pith.science/paper/GUBZLR7I"},"agent_actions":{"view_html":"https://pith.science/pith/GUBZLR7I7JL7LIHEP2OLWJ7VB6","download_json":"https://pith.science/pith/GUBZLR7I7JL7LIHEP2OLWJ7VB6.json","view_paper":"https://pith.science/paper/GUBZLR7I","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2607.04139&json=true","fetch_graph":"https://pith.science/api/pith-number/GUBZLR7I7JL7LIHEP2OLWJ7VB6/graph.json","fetch_events":"https://pith.science/api/pith-number/GUBZLR7I7JL7LIHEP2OLWJ7VB6/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/GUBZLR7I7JL7LIHEP2OLWJ7VB6/action/timestamp_anchor","attest_storage":"https://pith.science/pith/GUBZLR7I7JL7LIHEP2OLWJ7VB6/action/storage_attestation","attest_author":"https://pith.science/pith/GUBZLR7I7JL7LIHEP2OLWJ7VB6/action/author_attestation","sign_citation":"https://pith.science/pith/GUBZLR7I7JL7LIHEP2OLWJ7VB6/action/citation_signature","submit_replication":"https://pith.science/pith/GUBZLR7I7JL7LIHEP2OLWJ7VB6/action/replication_record"}},"created_at":"2026-07-07T02:18:59.254300+00:00","updated_at":"2026-07-07T02:18:59.254300+00:00"}