{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2021:X4ZVXBCYUGQR5DUFLIPDURYPSZ","short_pith_number":"pith:X4ZVXBCY","schema_version":"1.0","canonical_sha256":"bf335b8458a1a11e8e855a1e3a470f966003f3ca07185224e53f2c9459317751","source":{"kind":"arxiv","id":"2101.12037","version":1},"attestation_state":"computed","paper":{"title":"BENDR: using transformers and a contrastive self-supervised learning task to learn from massive amounts of EEG data","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.NE","q-bio.QM"],"primary_cat":"cs.LG","authors_text":"Demetres Kostas, Frank Rudzicz, Stephane Aroca-Ouellette","submitted_at":"2021-01-28T14:54:01Z","abstract_excerpt":"Deep neural networks (DNNs) used for brain-computer-interface (BCI) classification are commonly expected to learn general features when trained across a variety of contexts, such that these features could be fine-tuned to specific contexts. While some success is found in such an approach, we suggest that this interpretation is limited and an alternative would better leverage the newly (publicly) available massive EEG datasets. We consider how to adapt techniques and architectures used for language modelling (LM), that appear capable of ingesting awesome amounts of data, towards the development"},"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":"2101.12037","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2021-01-28T14:54:01Z","cross_cats_sorted":["cs.NE","q-bio.QM"],"title_canon_sha256":"0ed84349b7b9def241441e5966e75a9e8d1c397fd2ad4e9a98312f9f71dc8cc9","abstract_canon_sha256":"54a4828c65491a61c5d53e5ce8f06a78ad0ca7c2e3c9f22fdf3623af0479d3c1"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T02:10:33.707145Z","signature_b64":"SDsYQSHNvvbcmTt4WEuv/EbsQmqDXfHBZ3r3S9ahySLzyB7cnTiCs1KcwPUtXYwHuXNFydo5rC2S3UXB5l5oDg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"bf335b8458a1a11e8e855a1e3a470f966003f3ca07185224e53f2c9459317751","last_reissued_at":"2026-07-05T02:10:33.706623Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T02:10:33.706623Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"BENDR: using transformers and a contrastive self-supervised learning task to learn from massive amounts of EEG data","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.NE","q-bio.QM"],"primary_cat":"cs.LG","authors_text":"Demetres Kostas, Frank Rudzicz, Stephane Aroca-Ouellette","submitted_at":"2021-01-28T14:54:01Z","abstract_excerpt":"Deep neural networks (DNNs) used for brain-computer-interface (BCI) classification are commonly expected to learn general features when trained across a variety of contexts, such that these features could be fine-tuned to specific contexts. While some success is found in such an approach, we suggest that this interpretation is limited and an alternative would better leverage the newly (publicly) available massive EEG datasets. We consider how to adapt techniques and architectures used for language modelling (LM), that appear capable of ingesting awesome amounts of data, towards the development"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2101.12037","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/2101.12037/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":"2101.12037","created_at":"2026-07-05T02:10:33.706694+00:00"},{"alias_kind":"arxiv_version","alias_value":"2101.12037v1","created_at":"2026-07-05T02:10:33.706694+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2101.12037","created_at":"2026-07-05T02:10:33.706694+00:00"},{"alias_kind":"pith_short_12","alias_value":"X4ZVXBCYUGQR","created_at":"2026-07-05T02:10:33.706694+00:00"},{"alias_kind":"pith_short_16","alias_value":"X4ZVXBCYUGQR5DUF","created_at":"2026-07-05T02:10:33.706694+00:00"},{"alias_kind":"pith_short_8","alias_value":"X4ZVXBCY","created_at":"2026-07-05T02:10:33.706694+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":1,"internal_anchor_count":1,"sample":[{"citing_arxiv_id":"2607.06629","citing_title":"STST-JEPA: Shallow-Target Spatio-Temporal Joint Embedding Prediction Architecture For EEG Self-Supervised Learning","ref_index":7,"is_internal_anchor":true}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/X4ZVXBCYUGQR5DUFLIPDURYPSZ","json":"https://pith.science/pith/X4ZVXBCYUGQR5DUFLIPDURYPSZ.json","graph_json":"https://pith.science/api/pith-number/X4ZVXBCYUGQR5DUFLIPDURYPSZ/graph.json","events_json":"https://pith.science/api/pith-number/X4ZVXBCYUGQR5DUFLIPDURYPSZ/events.json","paper":"https://pith.science/paper/X4ZVXBCY"},"agent_actions":{"view_html":"https://pith.science/pith/X4ZVXBCYUGQR5DUFLIPDURYPSZ","download_json":"https://pith.science/pith/X4ZVXBCYUGQR5DUFLIPDURYPSZ.json","view_paper":"https://pith.science/paper/X4ZVXBCY","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2101.12037&json=true","fetch_graph":"https://pith.science/api/pith-number/X4ZVXBCYUGQR5DUFLIPDURYPSZ/graph.json","fetch_events":"https://pith.science/api/pith-number/X4ZVXBCYUGQR5DUFLIPDURYPSZ/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/X4ZVXBCYUGQR5DUFLIPDURYPSZ/action/timestamp_anchor","attest_storage":"https://pith.science/pith/X4ZVXBCYUGQR5DUFLIPDURYPSZ/action/storage_attestation","attest_author":"https://pith.science/pith/X4ZVXBCYUGQR5DUFLIPDURYPSZ/action/author_attestation","sign_citation":"https://pith.science/pith/X4ZVXBCYUGQR5DUFLIPDURYPSZ/action/citation_signature","submit_replication":"https://pith.science/pith/X4ZVXBCYUGQR5DUFLIPDURYPSZ/action/replication_record"}},"created_at":"2026-07-05T02:10:33.706694+00:00","updated_at":"2026-07-05T02:10:33.706694+00:00"}