{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2020:JHKSZZQID47LQFTPP24PQNOTSM","short_pith_number":"pith:JHKSZZQI","schema_version":"1.0","canonical_sha256":"49d52ce6081f3eb8166f7eb8f835d393341d06f1d9e1ad0b88ea833180d2c8e5","source":{"kind":"arxiv","id":"2007.00897","version":2},"attestation_state":"computed","paper":{"title":"Deep brain state classification of MEG data","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["eess.SP","q-bio.NC","stat.ML"],"primary_cat":"cs.LG","authors_text":"Caner Sahinli, Ismail Alaoui Abdellaoui, Jesus Garcia Fernandez, Siamak Mehrkanoon","submitted_at":"2020-07-02T05:51:57Z","abstract_excerpt":"Neuroimaging techniques have shown to be useful when studying the brain's activity. This paper uses Magnetoencephalography (MEG) data, provided by the Human Connectome Project (HCP), in combination with various deep artificial neural network models to perform brain decoding. More specifically, here we investigate to which extent can we infer the task performed by a subject based on its MEG data. Three models based on compact convolution, combined convolutional and long short-term architecture as well as a model based on multi-view learning that aims at fusing the outputs of the two stream netw"},"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":"2007.00897","kind":"arxiv","version":2},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2020-07-02T05:51:57Z","cross_cats_sorted":["eess.SP","q-bio.NC","stat.ML"],"title_canon_sha256":"6d43eb520114f0ece1bfd1e160d9fe5c1b61e5f26464bf80b7e2f4f5acc2a138","abstract_canon_sha256":"85a68c504de45f61eaf3e798fc117afbd0747672567da6f7898a660ef868ac8d"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T01:16:16.713354Z","signature_b64":"jTXj2zoWvcfIlXIX1Ju95NVgQQi/LcHqCxbPpTIOdLt7QHGPqhxdZrziDv3uMZA0Qx9msMNGdLvMgi02UrIkCQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"49d52ce6081f3eb8166f7eb8f835d393341d06f1d9e1ad0b88ea833180d2c8e5","last_reissued_at":"2026-07-05T01:16:16.712901Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T01:16:16.712901Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Deep brain state classification of MEG data","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["eess.SP","q-bio.NC","stat.ML"],"primary_cat":"cs.LG","authors_text":"Caner Sahinli, Ismail Alaoui Abdellaoui, Jesus Garcia Fernandez, Siamak Mehrkanoon","submitted_at":"2020-07-02T05:51:57Z","abstract_excerpt":"Neuroimaging techniques have shown to be useful when studying the brain's activity. This paper uses Magnetoencephalography (MEG) data, provided by the Human Connectome Project (HCP), in combination with various deep artificial neural network models to perform brain decoding. More specifically, here we investigate to which extent can we infer the task performed by a subject based on its MEG data. Three models based on compact convolution, combined convolutional and long short-term architecture as well as a model based on multi-view learning that aims at fusing the outputs of the two stream netw"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2007.00897","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/2007.00897/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":"2007.00897","created_at":"2026-07-05T01:16:16.712958+00:00"},{"alias_kind":"arxiv_version","alias_value":"2007.00897v2","created_at":"2026-07-05T01:16:16.712958+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2007.00897","created_at":"2026-07-05T01:16:16.712958+00:00"},{"alias_kind":"pith_short_12","alias_value":"JHKSZZQID47L","created_at":"2026-07-05T01:16:16.712958+00:00"},{"alias_kind":"pith_short_16","alias_value":"JHKSZZQID47LQFTP","created_at":"2026-07-05T01:16:16.712958+00:00"},{"alias_kind":"pith_short_8","alias_value":"JHKSZZQI","created_at":"2026-07-05T01:16:16.712958+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":1,"internal_anchor_count":1,"sample":[{"citing_arxiv_id":"2501.11566","citing_title":"Artificial Neural Networks for Magnetoencephalography: A review of an emerging field","ref_index":59,"is_internal_anchor":true}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/JHKSZZQID47LQFTPP24PQNOTSM","json":"https://pith.science/pith/JHKSZZQID47LQFTPP24PQNOTSM.json","graph_json":"https://pith.science/api/pith-number/JHKSZZQID47LQFTPP24PQNOTSM/graph.json","events_json":"https://pith.science/api/pith-number/JHKSZZQID47LQFTPP24PQNOTSM/events.json","paper":"https://pith.science/paper/JHKSZZQI"},"agent_actions":{"view_html":"https://pith.science/pith/JHKSZZQID47LQFTPP24PQNOTSM","download_json":"https://pith.science/pith/JHKSZZQID47LQFTPP24PQNOTSM.json","view_paper":"https://pith.science/paper/JHKSZZQI","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2007.00897&json=true","fetch_graph":"https://pith.science/api/pith-number/JHKSZZQID47LQFTPP24PQNOTSM/graph.json","fetch_events":"https://pith.science/api/pith-number/JHKSZZQID47LQFTPP24PQNOTSM/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/JHKSZZQID47LQFTPP24PQNOTSM/action/timestamp_anchor","attest_storage":"https://pith.science/pith/JHKSZZQID47LQFTPP24PQNOTSM/action/storage_attestation","attest_author":"https://pith.science/pith/JHKSZZQID47LQFTPP24PQNOTSM/action/author_attestation","sign_citation":"https://pith.science/pith/JHKSZZQID47LQFTPP24PQNOTSM/action/citation_signature","submit_replication":"https://pith.science/pith/JHKSZZQID47LQFTPP24PQNOTSM/action/replication_record"}},"created_at":"2026-07-05T01:16:16.712958+00:00","updated_at":"2026-07-05T01:16:16.712958+00:00"}