{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2022:4JRGY6J2RRPB5E5Z5CTRDQCRJH","short_pith_number":"pith:4JRGY6J2","schema_version":"1.0","canonical_sha256":"e2626c793a8c5e1e93b9e8a711c05149d672f6990a2bc3214179ffbd5e4873cc","source":{"kind":"arxiv","id":"2207.06911","version":1},"attestation_state":"computed","paper":{"title":"Improving self-supervised pretraining models for epileptic seizure detection from EEG data","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.LG"],"primary_cat":"eess.SP","authors_text":"Krishna Prasad Miyapuram, Pankaj Pandey, Sudip Das","submitted_at":"2022-06-28T17:15:49Z","abstract_excerpt":"There is abundant medical data on the internet, most of which are unlabeled. Traditional supervised learning algorithms are often limited by the amount of labeled data, especially in the medical domain, where labeling is costly in terms of human processing and specialized experts needed to label them. They are also prone to human error and biased as a select few expert annotators label them. These issues are mitigated by Self-supervision, where we generate pseudo-labels from unlabelled data by seeing the data itself. This paper presents various self-supervision strategies to enhance the perfor"},"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":"2207.06911","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"eess.SP","submitted_at":"2022-06-28T17:15:49Z","cross_cats_sorted":["cs.LG"],"title_canon_sha256":"18314e603a46a8a00fe459ef9b69ff75f8be73874bed74fad4698da688a15784","abstract_canon_sha256":"c1712b03504ffdf32a4d2fe28604cf7f6e06a4bca571b92c143807341eb86216"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T04:40:18.957745Z","signature_b64":"GFMAsCQrdojtqbGmFkvrUoMEyd2qqoSUFz1vewkS8oGZNO/zSXW+RlkbqN20m9qm15II03qpBHwqK5AR1KLeDQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"e2626c793a8c5e1e93b9e8a711c05149d672f6990a2bc3214179ffbd5e4873cc","last_reissued_at":"2026-07-05T04:40:18.957291Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T04:40:18.957291Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Improving self-supervised pretraining models for epileptic seizure detection from EEG data","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.LG"],"primary_cat":"eess.SP","authors_text":"Krishna Prasad Miyapuram, Pankaj Pandey, Sudip Das","submitted_at":"2022-06-28T17:15:49Z","abstract_excerpt":"There is abundant medical data on the internet, most of which are unlabeled. Traditional supervised learning algorithms are often limited by the amount of labeled data, especially in the medical domain, where labeling is costly in terms of human processing and specialized experts needed to label them. They are also prone to human error and biased as a select few expert annotators label them. These issues are mitigated by Self-supervision, where we generate pseudo-labels from unlabelled data by seeing the data itself. This paper presents various self-supervision strategies to enhance the perfor"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2207.06911","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/2207.06911/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":"2207.06911","created_at":"2026-07-05T04:40:18.957356+00:00"},{"alias_kind":"arxiv_version","alias_value":"2207.06911v1","created_at":"2026-07-05T04:40:18.957356+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2207.06911","created_at":"2026-07-05T04:40:18.957356+00:00"},{"alias_kind":"pith_short_12","alias_value":"4JRGY6J2RRPB","created_at":"2026-07-05T04:40:18.957356+00:00"},{"alias_kind":"pith_short_16","alias_value":"4JRGY6J2RRPB5E5Z","created_at":"2026-07-05T04:40:18.957356+00:00"},{"alias_kind":"pith_short_8","alias_value":"4JRGY6J2","created_at":"2026-07-05T04:40:18.957356+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/4JRGY6J2RRPB5E5Z5CTRDQCRJH","json":"https://pith.science/pith/4JRGY6J2RRPB5E5Z5CTRDQCRJH.json","graph_json":"https://pith.science/api/pith-number/4JRGY6J2RRPB5E5Z5CTRDQCRJH/graph.json","events_json":"https://pith.science/api/pith-number/4JRGY6J2RRPB5E5Z5CTRDQCRJH/events.json","paper":"https://pith.science/paper/4JRGY6J2"},"agent_actions":{"view_html":"https://pith.science/pith/4JRGY6J2RRPB5E5Z5CTRDQCRJH","download_json":"https://pith.science/pith/4JRGY6J2RRPB5E5Z5CTRDQCRJH.json","view_paper":"https://pith.science/paper/4JRGY6J2","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2207.06911&json=true","fetch_graph":"https://pith.science/api/pith-number/4JRGY6J2RRPB5E5Z5CTRDQCRJH/graph.json","fetch_events":"https://pith.science/api/pith-number/4JRGY6J2RRPB5E5Z5CTRDQCRJH/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/4JRGY6J2RRPB5E5Z5CTRDQCRJH/action/timestamp_anchor","attest_storage":"https://pith.science/pith/4JRGY6J2RRPB5E5Z5CTRDQCRJH/action/storage_attestation","attest_author":"https://pith.science/pith/4JRGY6J2RRPB5E5Z5CTRDQCRJH/action/author_attestation","sign_citation":"https://pith.science/pith/4JRGY6J2RRPB5E5Z5CTRDQCRJH/action/citation_signature","submit_replication":"https://pith.science/pith/4JRGY6J2RRPB5E5Z5CTRDQCRJH/action/replication_record"}},"created_at":"2026-07-05T04:40:18.957356+00:00","updated_at":"2026-07-05T04:40:18.957356+00:00"}