{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2021:7Z4TS4DY3ABOEGHDG6VVATZC54","short_pith_number":"pith:7Z4TS4DY","schema_version":"1.0","canonical_sha256":"fe79397078d802e218e337ab504f22ef1c51c9774dad202980aacf0e292d1345","source":{"kind":"arxiv","id":"2102.09188","version":3},"attestation_state":"computed","paper":{"title":"Edge Sparse Basis Network: A Deep Learning Framework for EEG Source Localization","license":"http://creativecommons.org/licenses/by-sa/4.0/","headline":"","cross_cats":["eess.SP"],"primary_cat":"cs.LG","authors_text":"Chen Wei, Dante Mantini, Kexin Lou, Mingqi Zhao, Quanying Liu, Zhengyang Wang","submitted_at":"2021-02-18T07:07:59Z","abstract_excerpt":"EEG source localization is an important technical issue in EEG analysis. Despite many numerical methods existed for EEG source localization, they all rely on strong priors and the deep sources are intractable. Here we propose a deep learning framework using spatial basis function decomposition for EEG source localization. This framework combines the edge sparsity prior and Gaussian source basis, called Edge Sparse Basis Network (ESBN). The performance of ESBN is validated by both synthetic data and real EEG data during motor tasks. The results suggest that the supervised ESBN outperforms the t"},"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":"2102.09188","kind":"arxiv","version":3},"metadata":{"license":"http://creativecommons.org/licenses/by-sa/4.0/","primary_cat":"cs.LG","submitted_at":"2021-02-18T07:07:59Z","cross_cats_sorted":["eess.SP"],"title_canon_sha256":"2ef7d6e88c69fff10b9798e486e1beb9b6c03776400a8ac348a41d10dd131143","abstract_canon_sha256":"a49ee2a7408b8c2f69c4fb11114ddd0b1d9fbfdd58b9958a53270940ba1d8e7d"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T02:49:53.345300Z","signature_b64":"W63CRD3Im6L6C42vJ5bqgNm1lH7gk8WnUdUYmVraj42nhrHRKUxIH0wJbvk0moKjy4nDo/uD5gZTmeur71doCQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"fe79397078d802e218e337ab504f22ef1c51c9774dad202980aacf0e292d1345","last_reissued_at":"2026-07-05T02:49:53.344816Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T02:49:53.344816Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Edge Sparse Basis Network: A Deep Learning Framework for EEG Source Localization","license":"http://creativecommons.org/licenses/by-sa/4.0/","headline":"","cross_cats":["eess.SP"],"primary_cat":"cs.LG","authors_text":"Chen Wei, Dante Mantini, Kexin Lou, Mingqi Zhao, Quanying Liu, Zhengyang Wang","submitted_at":"2021-02-18T07:07:59Z","abstract_excerpt":"EEG source localization is an important technical issue in EEG analysis. Despite many numerical methods existed for EEG source localization, they all rely on strong priors and the deep sources are intractable. Here we propose a deep learning framework using spatial basis function decomposition for EEG source localization. This framework combines the edge sparsity prior and Gaussian source basis, called Edge Sparse Basis Network (ESBN). The performance of ESBN is validated by both synthetic data and real EEG data during motor tasks. The results suggest that the supervised ESBN outperforms the t"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2102.09188","kind":"arxiv","version":3},"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/2102.09188/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":"2102.09188","created_at":"2026-07-05T02:49:53.344872+00:00"},{"alias_kind":"arxiv_version","alias_value":"2102.09188v3","created_at":"2026-07-05T02:49:53.344872+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2102.09188","created_at":"2026-07-05T02:49:53.344872+00:00"},{"alias_kind":"pith_short_12","alias_value":"7Z4TS4DY3ABO","created_at":"2026-07-05T02:49:53.344872+00:00"},{"alias_kind":"pith_short_16","alias_value":"7Z4TS4DY3ABOEGHD","created_at":"2026-07-05T02:49:53.344872+00:00"},{"alias_kind":"pith_short_8","alias_value":"7Z4TS4DY","created_at":"2026-07-05T02:49:53.344872+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/7Z4TS4DY3ABOEGHDG6VVATZC54","json":"https://pith.science/pith/7Z4TS4DY3ABOEGHDG6VVATZC54.json","graph_json":"https://pith.science/api/pith-number/7Z4TS4DY3ABOEGHDG6VVATZC54/graph.json","events_json":"https://pith.science/api/pith-number/7Z4TS4DY3ABOEGHDG6VVATZC54/events.json","paper":"https://pith.science/paper/7Z4TS4DY"},"agent_actions":{"view_html":"https://pith.science/pith/7Z4TS4DY3ABOEGHDG6VVATZC54","download_json":"https://pith.science/pith/7Z4TS4DY3ABOEGHDG6VVATZC54.json","view_paper":"https://pith.science/paper/7Z4TS4DY","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2102.09188&json=true","fetch_graph":"https://pith.science/api/pith-number/7Z4TS4DY3ABOEGHDG6VVATZC54/graph.json","fetch_events":"https://pith.science/api/pith-number/7Z4TS4DY3ABOEGHDG6VVATZC54/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/7Z4TS4DY3ABOEGHDG6VVATZC54/action/timestamp_anchor","attest_storage":"https://pith.science/pith/7Z4TS4DY3ABOEGHDG6VVATZC54/action/storage_attestation","attest_author":"https://pith.science/pith/7Z4TS4DY3ABOEGHDG6VVATZC54/action/author_attestation","sign_citation":"https://pith.science/pith/7Z4TS4DY3ABOEGHDG6VVATZC54/action/citation_signature","submit_replication":"https://pith.science/pith/7Z4TS4DY3ABOEGHDG6VVATZC54/action/replication_record"}},"created_at":"2026-07-05T02:49:53.344872+00:00","updated_at":"2026-07-05T02:49:53.344872+00:00"}