{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2025:IOYUW5NSDXQP3PED2G5PKG67BH","merge_version":"pith-open-graph-merge-v1","event_count":2,"valid_event_count":2,"invalid_event_count":0,"equivocation_count":0,"current":{"canonical_record":{"metadata":{"abstract_canon_sha256":"e2c1295f8025cbf37daf359afce05999c8e672ecd9b9d0dd0121c17b1da0c767","cross_cats_sorted":["cs.LG"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CE","submitted_at":"2025-06-13T14:34:29Z","title_canon_sha256":"fdce0650d0900adc17e58feeb8239bc545f10bac6bc7faa1d1788b0180d2606a"},"schema_version":"1.0","source":{"id":"2506.11830","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2506.11830","created_at":"2026-07-05T11:21:11Z"},{"alias_kind":"arxiv_version","alias_value":"2506.11830v1","created_at":"2026-07-05T11:21:11Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2506.11830","created_at":"2026-07-05T11:21:11Z"},{"alias_kind":"pith_short_12","alias_value":"IOYUW5NSDXQP","created_at":"2026-07-05T11:21:11Z"},{"alias_kind":"pith_short_16","alias_value":"IOYUW5NSDXQP3PED","created_at":"2026-07-05T11:21:11Z"},{"alias_kind":"pith_short_8","alias_value":"IOYUW5NS","created_at":"2026-07-05T11:21:11Z"}],"graph_snapshots":[{"event_id":"sha256:e5c50318649cf1c9c4a06e9e3b3dec1821587409edd7b762e1322d43f3791b31","target":"graph","created_at":"2026-07-05T11:21:11Z","signer":{"key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signer_id":"pith.science","signer_type":"pith_registry"},"payload":{"graph_snapshot":{"author_claims":{"count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57","strong_count":0},"builder_version":"pith-number-builder-2026-05-17-v1","claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"formal_canon":{"evidence_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"integrity":{"available":true,"clean":true,"detectors_run":[],"endpoint":"/pith/2506.11830/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"The construction of large-scale, high-quality datasets is a fundamental prerequisite for developing robust and generalizable foundation models in motor imagery (MI)-based brain-computer interfaces (BCIs). However, EEG signals collected from different subjects and devices are often plagued by low signal-to-noise ratio, heterogeneity in electrode configurations, and substantial inter-subject variability, posing significant challenges for effective model training. In this paper, we propose CLEAN-MI, a scalable and systematic data construction pipeline for constructing large-scale, efficient, and ","authors_text":"Dingkun Liu, Dongrui Wu, Zhu Chen","cross_cats":["cs.LG"],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CE","submitted_at":"2025-06-13T14:34:29Z","title":"CLEAN-MI: A Scalable and Efficient Pipeline for Constructing High-Quality Neurodata in Motor Imagery Paradigm"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2506.11830","kind":"arxiv","version":1},"verdict":{"created_at":null,"id":null,"model_set":{},"one_line_summary":"","pipeline_version":null,"pith_extraction_headline":"","strongest_claim":"","weakest_assumption":""}},"verdict_id":null}}],"author_attestations":[],"timestamp_anchors":[],"storage_attestations":[],"citation_signatures":[],"replication_records":[],"corrections":[],"mirror_hints":[],"record_created":{"event_id":"sha256:05eb56337234c7273538bed7f7fe036656720e83987e016c0e6340e04da3d3b8","target":"record","created_at":"2026-07-05T11:21:11Z","signer":{"key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signer_id":"pith.science","signer_type":"pith_registry"},"payload":{"attestation_state":"computed","canonical_record":{"metadata":{"abstract_canon_sha256":"e2c1295f8025cbf37daf359afce05999c8e672ecd9b9d0dd0121c17b1da0c767","cross_cats_sorted":["cs.LG"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CE","submitted_at":"2025-06-13T14:34:29Z","title_canon_sha256":"fdce0650d0900adc17e58feeb8239bc545f10bac6bc7faa1d1788b0180d2606a"},"schema_version":"1.0","source":{"id":"2506.11830","kind":"arxiv","version":1}},"canonical_sha256":"43b14b75b21de0fdbc83d1baf51bdf09ce909ff1d861549f2b9a5dbc83c500a3","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"43b14b75b21de0fdbc83d1baf51bdf09ce909ff1d861549f2b9a5dbc83c500a3","first_computed_at":"2026-07-05T11:21:11.421325Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T11:21:11.421325Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"qlv++XGSpmrgZAI8UW/5lEn/O1ltonNdtevkYM4EfgydgPtoYaC5prQRoPxbXbkzo0PL/uztMka7tfVBvzfDDg==","signature_status":"signed_v1","signed_at":"2026-07-05T11:21:11.421798Z","signed_message":"canonical_sha256_bytes"},"source_id":"2506.11830","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:05eb56337234c7273538bed7f7fe036656720e83987e016c0e6340e04da3d3b8","sha256:e5c50318649cf1c9c4a06e9e3b3dec1821587409edd7b762e1322d43f3791b31"],"state_sha256":"97ebe1af42eb3ce0353a80b26f19f1706877c64d4d76bda24320b01455fec8dd"}