{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2024:A5JJTT55VUHRIZTMMUPEZ6W3VB","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":"b413da7e6e6e991e4bd1f079f7b4e9496dc01147628ddefad8b926020b8b3ff3","cross_cats_sorted":["cs.LG","q-bio.NC"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"eess.SP","submitted_at":"2024-04-25T18:00:46Z","title_canon_sha256":"7afa451aede65cbe4eca4881ab891eca46f08581a5efada8bc0fc4bfe83f619c"},"schema_version":"1.0","source":{"id":"2405.00719","kind":"arxiv","version":2}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2405.00719","created_at":"2026-07-05T09:27:21Z"},{"alias_kind":"arxiv_version","alias_value":"2405.00719v2","created_at":"2026-07-05T09:27:21Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2405.00719","created_at":"2026-07-05T09:27:21Z"},{"alias_kind":"pith_short_12","alias_value":"A5JJTT55VUHR","created_at":"2026-07-05T09:27:21Z"},{"alias_kind":"pith_short_16","alias_value":"A5JJTT55VUHRIZTM","created_at":"2026-07-05T09:27:21Z"},{"alias_kind":"pith_short_8","alias_value":"A5JJTT55","created_at":"2026-07-05T09:27:21Z"}],"graph_snapshots":[{"event_id":"sha256:27812503d7ae12725783705437d07323f3bdfd211a28978c474ef83e2a1ca805","target":"graph","created_at":"2026-07-05T09:27:21Z","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/2405.00719/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Effectively learning the temporal dynamics in electroencephalogram (EEG) signals is challenging yet essential for decoding brain activities using brain-computer interfaces (BCIs). Although Transformers are popular for their long-term sequential learning ability in the BCI field, most methods combining Transformers with convolutional neural networks (CNNs) fail to capture the coarse-to-fine temporal dynamics of EEG signals. To overcome this limitation, we introduce EEG-Deformer, which incorporates two main novel components into a CNN-Transformer: (1) a Hierarchical Coarse-to-Fine Transformer (H","authors_text":"Chengxuan Tong, Chenyu Liu, Cuntai Guan, Hao Sun, Rui Liu, Xinliang Zhou, Yi Ding, Yong Li","cross_cats":["cs.LG","q-bio.NC"],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"eess.SP","submitted_at":"2024-04-25T18:00:46Z","title":"EEG-Deformer: A Dense Convolutional Transformer for Brain-computer Interfaces"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2405.00719","kind":"arxiv","version":2},"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:fae631b0d2728ae9787dc9ee43834872e93f444a42b73780ec7569b38c99396b","target":"record","created_at":"2026-07-05T09:27:21Z","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":"b413da7e6e6e991e4bd1f079f7b4e9496dc01147628ddefad8b926020b8b3ff3","cross_cats_sorted":["cs.LG","q-bio.NC"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"eess.SP","submitted_at":"2024-04-25T18:00:46Z","title_canon_sha256":"7afa451aede65cbe4eca4881ab891eca46f08581a5efada8bc0fc4bfe83f619c"},"schema_version":"1.0","source":{"id":"2405.00719","kind":"arxiv","version":2}},"canonical_sha256":"075299cfbdad0f14666c651e4cfadba86af3e7a32dcdbc8c6fec2d9bb2659a0f","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"075299cfbdad0f14666c651e4cfadba86af3e7a32dcdbc8c6fec2d9bb2659a0f","first_computed_at":"2026-07-05T09:27:21.579687Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T09:27:21.579687Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"So5xoEk5MGs3C0RASAOtUT8hJ+II8K+nLh5LxQQVhckHUCbWDNEN1tBshv+tacLNVfcqH5YOu0eHfyX1R8uwBw==","signature_status":"signed_v1","signed_at":"2026-07-05T09:27:21.580151Z","signed_message":"canonical_sha256_bytes"},"source_id":"2405.00719","source_kind":"arxiv","source_version":2}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:fae631b0d2728ae9787dc9ee43834872e93f444a42b73780ec7569b38c99396b","sha256:27812503d7ae12725783705437d07323f3bdfd211a28978c474ef83e2a1ca805"],"state_sha256":"85d8e695b7af6019ad8b0b672298ca6636606c97c225ba05811fdf5dd29be46a"}