{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2025:B56YGISNZAI2YLBC6PX23L6UC2","short_pith_number":"pith:B56YGISN","canonical_record":{"source":{"id":"2507.06779","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.HC","submitted_at":"2025-07-09T12:11:19Z","cross_cats_sorted":["cs.LG"],"title_canon_sha256":"f21886479a77eef298f01e02cdb14891191a6b04dd3832ce3523706a1cf7ca32","abstract_canon_sha256":"b656c4511a862969cf51f536ce1bf45d79c6217e27c192139f8ae89f59f7f6aa"},"schema_version":"1.0"},"canonical_sha256":"0f7d83224dc811ac2c22f3efadafd416aa95c1e6c4ac30db0201c21b5a5bff7c","source":{"kind":"arxiv","id":"2507.06779","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2507.06779","created_at":"2026-07-05T11:34:22Z"},{"alias_kind":"arxiv_version","alias_value":"2507.06779v1","created_at":"2026-07-05T11:34:22Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2507.06779","created_at":"2026-07-05T11:34:22Z"},{"alias_kind":"pith_short_12","alias_value":"B56YGISNZAI2","created_at":"2026-07-05T11:34:22Z"},{"alias_kind":"pith_short_16","alias_value":"B56YGISNZAI2YLBC","created_at":"2026-07-05T11:34:22Z"},{"alias_kind":"pith_short_8","alias_value":"B56YGISN","created_at":"2026-07-05T11:34:22Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2025:B56YGISNZAI2YLBC6PX23L6UC2","target":"record","payload":{"canonical_record":{"source":{"id":"2507.06779","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.HC","submitted_at":"2025-07-09T12:11:19Z","cross_cats_sorted":["cs.LG"],"title_canon_sha256":"f21886479a77eef298f01e02cdb14891191a6b04dd3832ce3523706a1cf7ca32","abstract_canon_sha256":"b656c4511a862969cf51f536ce1bf45d79c6217e27c192139f8ae89f59f7f6aa"},"schema_version":"1.0"},"canonical_sha256":"0f7d83224dc811ac2c22f3efadafd416aa95c1e6c4ac30db0201c21b5a5bff7c","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T11:34:22.941422Z","signature_b64":"eja6SQWKBV77ptxDMpcVC5FQKtUSGszWseTS0FdGcQrkJadHqppCPixTwviVilbuCZPulOAwLmxwld3EjmoCDg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"0f7d83224dc811ac2c22f3efadafd416aa95c1e6c4ac30db0201c21b5a5bff7c","last_reissued_at":"2026-07-05T11:34:22.940690Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T11:34:22.940690Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2507.06779","source_version":1,"attestation_state":"computed"},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-07-05T11:34:22Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"A4+0J1nlwXIVxDEsSRBXgOMQL7bsNxRdW80xlg/EdmddA9N7kGE1Lv7TQZ2580kZG84tNORxcM4Oe1FdsnywDw==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-05T07:08:23.154884Z"},"content_sha256":"a1933a1cf882f7ce0369b7457e38255b291b9e6937f880edfedd1409db5b3785","schema_version":"1.0","event_id":"sha256:a1933a1cf882f7ce0369b7457e38255b291b9e6937f880edfedd1409db5b3785"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2025:B56YGISNZAI2YLBC6PX23L6UC2","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Tailoring deep learning for real-time brain-computer interfaces: From offline models to calibration-free online decoding","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.LG"],"primary_cat":"cs.HC","authors_text":"Bin Yang, Jan Zerfowski, Martin Wimpff","submitted_at":"2025-07-09T12:11:19Z","abstract_excerpt":"Despite the growing success of deep learning (DL) in offline brain-computer interfaces (BCIs), its adoption in real-time applications remains limited due to three primary challenges. First, most DL solutions are designed for offline decoding, making the transition to online decoding unclear. Second, the use of sliding windows in online decoding substantially increases computational complexity. Third, DL models typically require large amounts of training data, which are often scarce in BCI applications. To address these challenges and enable real-time, cross-subject decoding without subject-spe"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2507.06779","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/2507.06779/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"},"verdict_id":null},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-07-05T11:34:22Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"d5C6owHd+uNGwuwuzNMKrnqPmQ37SBwV1+brwTROtA99jGyhR4UmEAAK0rqSDTOwOzOzfoLPWND5d2m9XzyEDQ==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-05T07:08:23.155845Z"},"content_sha256":"03221bccf1694a7034e393951b9b2e051cdf886439ab92f6a65d6dad59cb0bb8","schema_version":"1.0","event_id":"sha256:03221bccf1694a7034e393951b9b2e051cdf886439ab92f6a65d6dad59cb0bb8"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/B56YGISNZAI2YLBC6PX23L6UC2/bundle.json","state_url":"https://pith.science/pith/B56YGISNZAI2YLBC6PX23L6UC2/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/B56YGISNZAI2YLBC6PX23L6UC2/bundle.json","status":"primary"}],"public_keys":[{"key_id":"pith-v1-2026-05","algorithm":"ed25519","format":"raw","public_key_b64":"stVStoiQhXFxp4s2pdzPNoqVNBMojDU/fJ2db5S3CbM=","public_key_hex":"b2d552b68890857171a78b36a5dccf368a953413288c353f7c9d9d6f94b709b3","fingerprint_sha256_b32_first128bits":"RVFV5Z2OI2J3ZUO7ERDEBCYNKS","fingerprint_sha256_hex":"8d4b5ee74e4693bcd1df2446408b0d54","rotates_at":null,"url":"https://pith.science/pith-signing-key.json","notes":"Pith uses this Ed25519 key to sign canonical record SHA-256 digests. Verify with: ed25519_verify(public_key, message=canonical_sha256_bytes, signature=base64decode(signature_b64))."}],"merge_version":"pith-open-graph-merge-v1","built_at":"2026-08-05T07:08:23Z","links":{"resolver":"https://pith.science/pith/B56YGISNZAI2YLBC6PX23L6UC2","bundle":"https://pith.science/pith/B56YGISNZAI2YLBC6PX23L6UC2/bundle.json","state":"https://pith.science/pith/B56YGISNZAI2YLBC6PX23L6UC2/state.json","well_known_bundle":"https://pith.science/.well-known/pith/B56YGISNZAI2YLBC6PX23L6UC2/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2025:B56YGISNZAI2YLBC6PX23L6UC2","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":"b656c4511a862969cf51f536ce1bf45d79c6217e27c192139f8ae89f59f7f6aa","cross_cats_sorted":["cs.LG"],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.HC","submitted_at":"2025-07-09T12:11:19Z","title_canon_sha256":"f21886479a77eef298f01e02cdb14891191a6b04dd3832ce3523706a1cf7ca32"},"schema_version":"1.0","source":{"id":"2507.06779","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2507.06779","created_at":"2026-07-05T11:34:22Z"},{"alias_kind":"arxiv_version","alias_value":"2507.06779v1","created_at":"2026-07-05T11:34:22Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2507.06779","created_at":"2026-07-05T11:34:22Z"},{"alias_kind":"pith_short_12","alias_value":"B56YGISNZAI2","created_at":"2026-07-05T11:34:22Z"},{"alias_kind":"pith_short_16","alias_value":"B56YGISNZAI2YLBC","created_at":"2026-07-05T11:34:22Z"},{"alias_kind":"pith_short_8","alias_value":"B56YGISN","created_at":"2026-07-05T11:34:22Z"}],"graph_snapshots":[{"event_id":"sha256:03221bccf1694a7034e393951b9b2e051cdf886439ab92f6a65d6dad59cb0bb8","target":"graph","created_at":"2026-07-05T11:34:22Z","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/2507.06779/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Despite the growing success of deep learning (DL) in offline brain-computer interfaces (BCIs), its adoption in real-time applications remains limited due to three primary challenges. First, most DL solutions are designed for offline decoding, making the transition to online decoding unclear. Second, the use of sliding windows in online decoding substantially increases computational complexity. Third, DL models typically require large amounts of training data, which are often scarce in BCI applications. To address these challenges and enable real-time, cross-subject decoding without subject-spe","authors_text":"Bin Yang, Jan Zerfowski, Martin Wimpff","cross_cats":["cs.LG"],"headline":"","license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.HC","submitted_at":"2025-07-09T12:11:19Z","title":"Tailoring deep learning for real-time brain-computer interfaces: From offline models to calibration-free online decoding"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2507.06779","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:a1933a1cf882f7ce0369b7457e38255b291b9e6937f880edfedd1409db5b3785","target":"record","created_at":"2026-07-05T11:34:22Z","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":"b656c4511a862969cf51f536ce1bf45d79c6217e27c192139f8ae89f59f7f6aa","cross_cats_sorted":["cs.LG"],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.HC","submitted_at":"2025-07-09T12:11:19Z","title_canon_sha256":"f21886479a77eef298f01e02cdb14891191a6b04dd3832ce3523706a1cf7ca32"},"schema_version":"1.0","source":{"id":"2507.06779","kind":"arxiv","version":1}},"canonical_sha256":"0f7d83224dc811ac2c22f3efadafd416aa95c1e6c4ac30db0201c21b5a5bff7c","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"0f7d83224dc811ac2c22f3efadafd416aa95c1e6c4ac30db0201c21b5a5bff7c","first_computed_at":"2026-07-05T11:34:22.940690Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T11:34:22.940690Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"eja6SQWKBV77ptxDMpcVC5FQKtUSGszWseTS0FdGcQrkJadHqppCPixTwviVilbuCZPulOAwLmxwld3EjmoCDg==","signature_status":"signed_v1","signed_at":"2026-07-05T11:34:22.941422Z","signed_message":"canonical_sha256_bytes"},"source_id":"2507.06779","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:a1933a1cf882f7ce0369b7457e38255b291b9e6937f880edfedd1409db5b3785","sha256:03221bccf1694a7034e393951b9b2e051cdf886439ab92f6a65d6dad59cb0bb8"],"state_sha256":"10385f72f875429adbc3d683b587a0045d4214a782276598d6aa4a9c2a91ee9a"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"BejMIKTzAUuzSchR5GmwhWAu7Stk/bmnNkiK+p41+ftB6rYkgRWIvaLZeIBa11IlCcET8sYb0jpB/Ej0+PzQDQ==","signed_message":"bundle_sha256_bytes","signed_at":"2026-08-05T07:08:23.163683Z","bundle_sha256":"0589971f43763702829eefa34c21194d4f834428be9f90f211ff75fe99f3715f"}}