{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2023:EM2FQ3RQM6UDW344HZVA4QWJNL","short_pith_number":"pith:EM2FQ3RQ","schema_version":"1.0","canonical_sha256":"2334586e3067a83b6f9c3e6a0e42c96ad92106bbd2f676cbc15f683e481f7046","source":{"kind":"arxiv","id":"2309.12056","version":2},"attestation_state":"computed","paper":{"title":"BELT:Bootstrapping Electroencephalography-to-Language Decoding and Zero-Shot Sentiment Classification by Natural Language Supervision","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.CL","eess.SP"],"primary_cat":"cs.AI","authors_text":"Chin-Teng Lin, Jinzhao Zhou, Yiqun Duan, Yu-Cheng Chang, Yu-Kai Wang","submitted_at":"2023-09-21T13:24:01Z","abstract_excerpt":"This paper presents BELT, a novel model and learning framework for the pivotal topic of brain-to-language translation research. The translation from noninvasive brain signals into readable natural language has the potential to promote the application scenario as well as the development of brain-computer interfaces (BCI) as a whole. The critical problem in brain signal decoding or brain-to-language translation is the acquisition of semantically appropriate and discriminative EEG representation from a dataset of limited scale and quality. The proposed BELT method is a generic and efficient frame"},"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":"2309.12056","kind":"arxiv","version":2},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.AI","submitted_at":"2023-09-21T13:24:01Z","cross_cats_sorted":["cs.CL","eess.SP"],"title_canon_sha256":"b0b42e6096d36ddb65da5e0d769e666cde56a516f0eee375b23a1f716f0277b5","abstract_canon_sha256":"9d4f595a1e185a0a6bc5801b4610f9b931e44bae545eb4f3c0de0f6128de3d0e"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T07:22:23.434419Z","signature_b64":"iz1C2p6mJrAaYhEQZ/Vy0euJQotPlti3yyGh8xshb59tf8XwsL74aWjMe7NQ9dS8LcnXdTnXWxQRU5IYNPgSBw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"2334586e3067a83b6f9c3e6a0e42c96ad92106bbd2f676cbc15f683e481f7046","last_reissued_at":"2026-07-05T07:22:23.433933Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T07:22:23.433933Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"BELT:Bootstrapping Electroencephalography-to-Language Decoding and Zero-Shot Sentiment Classification by Natural Language Supervision","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.CL","eess.SP"],"primary_cat":"cs.AI","authors_text":"Chin-Teng Lin, Jinzhao Zhou, Yiqun Duan, Yu-Cheng Chang, Yu-Kai Wang","submitted_at":"2023-09-21T13:24:01Z","abstract_excerpt":"This paper presents BELT, a novel model and learning framework for the pivotal topic of brain-to-language translation research. The translation from noninvasive brain signals into readable natural language has the potential to promote the application scenario as well as the development of brain-computer interfaces (BCI) as a whole. The critical problem in brain signal decoding or brain-to-language translation is the acquisition of semantically appropriate and discriminative EEG representation from a dataset of limited scale and quality. The proposed BELT method is a generic and efficient frame"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2309.12056","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":""},"integrity":{"clean":true,"summary":{"advisory":0,"critical":0,"by_detector":{},"informational":0},"endpoint":"/pith/2309.12056/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":"2309.12056","created_at":"2026-07-05T07:22:23.433985+00:00"},{"alias_kind":"arxiv_version","alias_value":"2309.12056v2","created_at":"2026-07-05T07:22:23.433985+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2309.12056","created_at":"2026-07-05T07:22:23.433985+00:00"},{"alias_kind":"pith_short_12","alias_value":"EM2FQ3RQM6UD","created_at":"2026-07-05T07:22:23.433985+00:00"},{"alias_kind":"pith_short_16","alias_value":"EM2FQ3RQM6UDW344","created_at":"2026-07-05T07:22:23.433985+00:00"},{"alias_kind":"pith_short_8","alias_value":"EM2FQ3RQ","created_at":"2026-07-05T07:22:23.433985+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/EM2FQ3RQM6UDW344HZVA4QWJNL","json":"https://pith.science/pith/EM2FQ3RQM6UDW344HZVA4QWJNL.json","graph_json":"https://pith.science/api/pith-number/EM2FQ3RQM6UDW344HZVA4QWJNL/graph.json","events_json":"https://pith.science/api/pith-number/EM2FQ3RQM6UDW344HZVA4QWJNL/events.json","paper":"https://pith.science/paper/EM2FQ3RQ"},"agent_actions":{"view_html":"https://pith.science/pith/EM2FQ3RQM6UDW344HZVA4QWJNL","download_json":"https://pith.science/pith/EM2FQ3RQM6UDW344HZVA4QWJNL.json","view_paper":"https://pith.science/paper/EM2FQ3RQ","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2309.12056&json=true","fetch_graph":"https://pith.science/api/pith-number/EM2FQ3RQM6UDW344HZVA4QWJNL/graph.json","fetch_events":"https://pith.science/api/pith-number/EM2FQ3RQM6UDW344HZVA4QWJNL/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/EM2FQ3RQM6UDW344HZVA4QWJNL/action/timestamp_anchor","attest_storage":"https://pith.science/pith/EM2FQ3RQM6UDW344HZVA4QWJNL/action/storage_attestation","attest_author":"https://pith.science/pith/EM2FQ3RQM6UDW344HZVA4QWJNL/action/author_attestation","sign_citation":"https://pith.science/pith/EM2FQ3RQM6UDW344HZVA4QWJNL/action/citation_signature","submit_replication":"https://pith.science/pith/EM2FQ3RQM6UDW344HZVA4QWJNL/action/replication_record"}},"created_at":"2026-07-05T07:22:23.433985+00:00","updated_at":"2026-07-05T07:22:23.433985+00:00"}