{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2025:QE2AR7ROYMP4XIWT5RF52KJOUA","short_pith_number":"pith:QE2AR7RO","schema_version":"1.0","canonical_sha256":"813408fe2ec31fcba2d3ec4bdd292ea03895afe404b8db1752b146505512c8e5","source":{"kind":"arxiv","id":"2504.17352","version":1},"attestation_state":"computed","paper":{"title":"The Riemannian Means Field Classifier for EEG-Based BCI Data","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["eess.SP"],"primary_cat":"cs.HC","authors_text":"Anton Andreev (GIPSA-lab), GIPSA-lab), Gr\\'egoire Cattan, Marco Congedo (GIPSA-VIBS","submitted_at":"2025-04-24T08:13:56Z","abstract_excerpt":"A substantial amount of research has demonstrated the robustness and accuracy of the Riemannian minimum distance to mean (MDM) classifier for all kinds of EEG-based brain--computer interfaces (BCIs). This classifier is simple, fully deterministic, robust to noise, computationally efficient, and prone to transfer learning. Its training is very simple, requiring just the computation of a geometric mean of a symmetric positive-definite (SPD) matrix per class. We propose an improvement of the MDM involving a number of power means of SPD matrices instead of the sole geometric mean. By the analysis "},"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":"2504.17352","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.HC","submitted_at":"2025-04-24T08:13:56Z","cross_cats_sorted":["eess.SP"],"title_canon_sha256":"004d9841dbb4b25d3e521f5149bc2916982c2cddef96cc1a8a99ad3f20cdf34b","abstract_canon_sha256":"804450a9ddb091a9a3a897a1c4e103bb96a670375262a5f39ef4e90d1c123bbe"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T10:53:27.272843Z","signature_b64":"wg521LhPLy8oJl3Po92w7mP1Xg38mr+EDour9RxAjtIELojy8NLElT/oGtzvejhO/1cGQdg3OO7rPY0pgn6dAA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"813408fe2ec31fcba2d3ec4bdd292ea03895afe404b8db1752b146505512c8e5","last_reissued_at":"2026-07-05T10:53:27.272283Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T10:53:27.272283Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"The Riemannian Means Field Classifier for EEG-Based BCI Data","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["eess.SP"],"primary_cat":"cs.HC","authors_text":"Anton Andreev (GIPSA-lab), GIPSA-lab), Gr\\'egoire Cattan, Marco Congedo (GIPSA-VIBS","submitted_at":"2025-04-24T08:13:56Z","abstract_excerpt":"A substantial amount of research has demonstrated the robustness and accuracy of the Riemannian minimum distance to mean (MDM) classifier for all kinds of EEG-based brain--computer interfaces (BCIs). This classifier is simple, fully deterministic, robust to noise, computationally efficient, and prone to transfer learning. Its training is very simple, requiring just the computation of a geometric mean of a symmetric positive-definite (SPD) matrix per class. We propose an improvement of the MDM involving a number of power means of SPD matrices instead of the sole geometric mean. By the analysis "},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2504.17352","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/2504.17352/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":"2504.17352","created_at":"2026-07-05T10:53:27.272351+00:00"},{"alias_kind":"arxiv_version","alias_value":"2504.17352v1","created_at":"2026-07-05T10:53:27.272351+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2504.17352","created_at":"2026-07-05T10:53:27.272351+00:00"},{"alias_kind":"pith_short_12","alias_value":"QE2AR7ROYMP4","created_at":"2026-07-05T10:53:27.272351+00:00"},{"alias_kind":"pith_short_16","alias_value":"QE2AR7ROYMP4XIWT","created_at":"2026-07-05T10:53:27.272351+00:00"},{"alias_kind":"pith_short_8","alias_value":"QE2AR7RO","created_at":"2026-07-05T10:53:27.272351+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/QE2AR7ROYMP4XIWT5RF52KJOUA","json":"https://pith.science/pith/QE2AR7ROYMP4XIWT5RF52KJOUA.json","graph_json":"https://pith.science/api/pith-number/QE2AR7ROYMP4XIWT5RF52KJOUA/graph.json","events_json":"https://pith.science/api/pith-number/QE2AR7ROYMP4XIWT5RF52KJOUA/events.json","paper":"https://pith.science/paper/QE2AR7RO"},"agent_actions":{"view_html":"https://pith.science/pith/QE2AR7ROYMP4XIWT5RF52KJOUA","download_json":"https://pith.science/pith/QE2AR7ROYMP4XIWT5RF52KJOUA.json","view_paper":"https://pith.science/paper/QE2AR7RO","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2504.17352&json=true","fetch_graph":"https://pith.science/api/pith-number/QE2AR7ROYMP4XIWT5RF52KJOUA/graph.json","fetch_events":"https://pith.science/api/pith-number/QE2AR7ROYMP4XIWT5RF52KJOUA/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/QE2AR7ROYMP4XIWT5RF52KJOUA/action/timestamp_anchor","attest_storage":"https://pith.science/pith/QE2AR7ROYMP4XIWT5RF52KJOUA/action/storage_attestation","attest_author":"https://pith.science/pith/QE2AR7ROYMP4XIWT5RF52KJOUA/action/author_attestation","sign_citation":"https://pith.science/pith/QE2AR7ROYMP4XIWT5RF52KJOUA/action/citation_signature","submit_replication":"https://pith.science/pith/QE2AR7ROYMP4XIWT5RF52KJOUA/action/replication_record"}},"created_at":"2026-07-05T10:53:27.272351+00:00","updated_at":"2026-07-05T10:53:27.272351+00:00"}