{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2016:LTDEEHZAHSE55EBIJ3P3BV2J64","short_pith_number":"pith:LTDEEHZA","schema_version":"1.0","canonical_sha256":"5cc6421f203c89de90284edfb0d749f7185debe0bfd32421ee8f0f610ffd3d3c","source":{"kind":"arxiv","id":"1610.01636","version":1},"attestation_state":"computed","paper":{"title":"An inversion method based on random sampling for real-time MEG neuroimaging","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["q-bio.NC"],"primary_cat":"physics.med-ph","authors_text":"Annalisa Pascarella, Francesca Pitolli","submitted_at":"2016-10-05T20:33:29Z","abstract_excerpt":"The MagnetoEncephaloGraphy (MEG) has gained great interest in neurorehabilitation training due to its high temporal resolution. The challenge is to localize the active regions of the brain in a fast and accurate way. In this paper we use an inversion method based on random spatial sampling to solve the real-time MEG inverse problem. Several numerical tests on synthetic but realistic data show that the method takes just a few hundredths of a second on a laptop to produce an accurate map of the electric activity inside the brain. Moreover, it requires very little memory storage. For this reasons"},"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":"1610.01636","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"physics.med-ph","submitted_at":"2016-10-05T20:33:29Z","cross_cats_sorted":["q-bio.NC"],"title_canon_sha256":"ba0eb89dc012fc86148336a9206c10973d8017000e013d74311830c85f65747d","abstract_canon_sha256":"2b27704d6570153a97236602ef8073e1dae0f5835110af5d05d9a7117d6a8be9"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T01:03:05.366353Z","signature_b64":"sMp6b7FxZeoLQ4CXi/TQRHcT4lYfkfXsgFeKqTqRY7mdbOhd8djuwXFoogqx851/N5++yAM8tWzdBptfVY3QBQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"5cc6421f203c89de90284edfb0d749f7185debe0bfd32421ee8f0f610ffd3d3c","last_reissued_at":"2026-05-18T01:03:05.365831Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T01:03:05.365831Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"An inversion method based on random sampling for real-time MEG neuroimaging","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["q-bio.NC"],"primary_cat":"physics.med-ph","authors_text":"Annalisa Pascarella, Francesca Pitolli","submitted_at":"2016-10-05T20:33:29Z","abstract_excerpt":"The MagnetoEncephaloGraphy (MEG) has gained great interest in neurorehabilitation training due to its high temporal resolution. The challenge is to localize the active regions of the brain in a fast and accurate way. In this paper we use an inversion method based on random spatial sampling to solve the real-time MEG inverse problem. Several numerical tests on synthetic but realistic data show that the method takes just a few hundredths of a second on a laptop to produce an accurate map of the electric activity inside the brain. Moreover, it requires very little memory storage. For this reasons"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1610.01636","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":""},"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":"1610.01636","created_at":"2026-05-18T01:03:05.365907+00:00"},{"alias_kind":"arxiv_version","alias_value":"1610.01636v1","created_at":"2026-05-18T01:03:05.365907+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1610.01636","created_at":"2026-05-18T01:03:05.365907+00:00"},{"alias_kind":"pith_short_12","alias_value":"LTDEEHZAHSE5","created_at":"2026-05-18T12:30:29.479603+00:00"},{"alias_kind":"pith_short_16","alias_value":"LTDEEHZAHSE55EBI","created_at":"2026-05-18T12:30:29.479603+00:00"},{"alias_kind":"pith_short_8","alias_value":"LTDEEHZA","created_at":"2026-05-18T12:30:29.479603+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/LTDEEHZAHSE55EBIJ3P3BV2J64","json":"https://pith.science/pith/LTDEEHZAHSE55EBIJ3P3BV2J64.json","graph_json":"https://pith.science/api/pith-number/LTDEEHZAHSE55EBIJ3P3BV2J64/graph.json","events_json":"https://pith.science/api/pith-number/LTDEEHZAHSE55EBIJ3P3BV2J64/events.json","paper":"https://pith.science/paper/LTDEEHZA"},"agent_actions":{"view_html":"https://pith.science/pith/LTDEEHZAHSE55EBIJ3P3BV2J64","download_json":"https://pith.science/pith/LTDEEHZAHSE55EBIJ3P3BV2J64.json","view_paper":"https://pith.science/paper/LTDEEHZA","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=1610.01636&json=true","fetch_graph":"https://pith.science/api/pith-number/LTDEEHZAHSE55EBIJ3P3BV2J64/graph.json","fetch_events":"https://pith.science/api/pith-number/LTDEEHZAHSE55EBIJ3P3BV2J64/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/LTDEEHZAHSE55EBIJ3P3BV2J64/action/timestamp_anchor","attest_storage":"https://pith.science/pith/LTDEEHZAHSE55EBIJ3P3BV2J64/action/storage_attestation","attest_author":"https://pith.science/pith/LTDEEHZAHSE55EBIJ3P3BV2J64/action/author_attestation","sign_citation":"https://pith.science/pith/LTDEEHZAHSE55EBIJ3P3BV2J64/action/citation_signature","submit_replication":"https://pith.science/pith/LTDEEHZAHSE55EBIJ3P3BV2J64/action/replication_record"}},"created_at":"2026-05-18T01:03:05.365907+00:00","updated_at":"2026-05-18T01:03:05.365907+00:00"}