{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2018:YHRVQKZYCDOM5H5VFSWKKOCPFD","short_pith_number":"pith:YHRVQKZY","schema_version":"1.0","canonical_sha256":"c1e3582b3810dcce9fb52caca5384f28dcee0269f6b31cd86aff5a8bfdd2fbb4","source":{"kind":"arxiv","id":"1806.09532","version":2},"attestation_state":"computed","paper":{"title":"Cross-paradigm pretraining of convolutional networks improves intracranial EEG decoding","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.HC"],"primary_cat":"q-bio.NC","authors_text":"Andreas Schulze-Bonhage, Ji\\v{r}\\'i Hammer, Joos Behncke, Martin V\\\"olker, Petr Marusi\\v{c}, Robin Tibor Schirrmeister, Tonio Ball, Wolfram Burgard","submitted_at":"2018-06-20T11:34:36Z","abstract_excerpt":"When it comes to the classification of brain signals in real-life applications, the training and the prediction data are often described by different distributions. Furthermore, diverse data sets, e.g., recorded from various subjects or tasks, can even exhibit distinct feature spaces. The fact that data that have to be classified are often only available in small amounts reinforces the need for techniques to generalize learned information, as performances of brain-computer interfaces (BCIs) are enhanced by increasing quantity of available data. In this paper, we apply transfer learning to a fr"},"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":"1806.09532","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"q-bio.NC","submitted_at":"2018-06-20T11:34:36Z","cross_cats_sorted":["cs.HC"],"title_canon_sha256":"2af71b72088f4c8b6829e03581b41f63d97eafe5bd1a179428e7c6c04bfd176d","abstract_canon_sha256":"5539365f660924eb0f6daddf3fed5dd96496e160538c56bf1be4a57b5a642e56"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T00:10:16.506933Z","signature_b64":"2GLT8E6lQGSxzkTyyqUfMxkSb+df+/lZtY4ti10iJKPYrlykdWsFRn03x5MNE88W9Dw1F0j7NMxvo3nM+mgMBg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"c1e3582b3810dcce9fb52caca5384f28dcee0269f6b31cd86aff5a8bfdd2fbb4","last_reissued_at":"2026-05-18T00:10:16.506291Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T00:10:16.506291Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Cross-paradigm pretraining of convolutional networks improves intracranial EEG decoding","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.HC"],"primary_cat":"q-bio.NC","authors_text":"Andreas Schulze-Bonhage, Ji\\v{r}\\'i Hammer, Joos Behncke, Martin V\\\"olker, Petr Marusi\\v{c}, Robin Tibor Schirrmeister, Tonio Ball, Wolfram Burgard","submitted_at":"2018-06-20T11:34:36Z","abstract_excerpt":"When it comes to the classification of brain signals in real-life applications, the training and the prediction data are often described by different distributions. Furthermore, diverse data sets, e.g., recorded from various subjects or tasks, can even exhibit distinct feature spaces. The fact that data that have to be classified are often only available in small amounts reinforces the need for techniques to generalize learned information, as performances of brain-computer interfaces (BCIs) are enhanced by increasing quantity of available data. In this paper, we apply transfer learning to a fr"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1806.09532","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":""},"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":"1806.09532","created_at":"2026-05-18T00:10:16.506402+00:00"},{"alias_kind":"arxiv_version","alias_value":"1806.09532v2","created_at":"2026-05-18T00:10:16.506402+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1806.09532","created_at":"2026-05-18T00:10:16.506402+00:00"},{"alias_kind":"pith_short_12","alias_value":"YHRVQKZYCDOM","created_at":"2026-05-18T12:33:04.347982+00:00"},{"alias_kind":"pith_short_16","alias_value":"YHRVQKZYCDOM5H5V","created_at":"2026-05-18T12:33:04.347982+00:00"},{"alias_kind":"pith_short_8","alias_value":"YHRVQKZY","created_at":"2026-05-18T12:33:04.347982+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/YHRVQKZYCDOM5H5VFSWKKOCPFD","json":"https://pith.science/pith/YHRVQKZYCDOM5H5VFSWKKOCPFD.json","graph_json":"https://pith.science/api/pith-number/YHRVQKZYCDOM5H5VFSWKKOCPFD/graph.json","events_json":"https://pith.science/api/pith-number/YHRVQKZYCDOM5H5VFSWKKOCPFD/events.json","paper":"https://pith.science/paper/YHRVQKZY"},"agent_actions":{"view_html":"https://pith.science/pith/YHRVQKZYCDOM5H5VFSWKKOCPFD","download_json":"https://pith.science/pith/YHRVQKZYCDOM5H5VFSWKKOCPFD.json","view_paper":"https://pith.science/paper/YHRVQKZY","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=1806.09532&json=true","fetch_graph":"https://pith.science/api/pith-number/YHRVQKZYCDOM5H5VFSWKKOCPFD/graph.json","fetch_events":"https://pith.science/api/pith-number/YHRVQKZYCDOM5H5VFSWKKOCPFD/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/YHRVQKZYCDOM5H5VFSWKKOCPFD/action/timestamp_anchor","attest_storage":"https://pith.science/pith/YHRVQKZYCDOM5H5VFSWKKOCPFD/action/storage_attestation","attest_author":"https://pith.science/pith/YHRVQKZYCDOM5H5VFSWKKOCPFD/action/author_attestation","sign_citation":"https://pith.science/pith/YHRVQKZYCDOM5H5VFSWKKOCPFD/action/citation_signature","submit_replication":"https://pith.science/pith/YHRVQKZYCDOM5H5VFSWKKOCPFD/action/replication_record"}},"created_at":"2026-05-18T00:10:16.506402+00:00","updated_at":"2026-05-18T00:10:16.506402+00:00"}