{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2018:6YVCX4NWBBVWFFG2PR36YOHGME","short_pith_number":"pith:6YVCX4NW","canonical_record":{"source":{"id":"1810.10353","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2018-10-22T07:39:12Z","cross_cats_sorted":["cs.HC"],"title_canon_sha256":"45d845b65b5037083acb5fd163330eba64fa7bae28546803eb96a42bdec17fbf","abstract_canon_sha256":"1e3745d548afb1b299919689ba4e5ed733bbf705d66bd3b318d7b05fe0c793df"},"schema_version":"1.0"},"canonical_sha256":"f62a2bf1b6086b6294da7c77ec38e6610884772a974cd9e2a339cd8d8142b445","source":{"kind":"arxiv","id":"1810.10353","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1810.10353","created_at":"2026-05-18T00:02:23Z"},{"alias_kind":"arxiv_version","alias_value":"1810.10353v1","created_at":"2026-05-18T00:02:23Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1810.10353","created_at":"2026-05-18T00:02:23Z"},{"alias_kind":"pith_short_12","alias_value":"6YVCX4NWBBVW","created_at":"2026-05-18T12:32:11Z"},{"alias_kind":"pith_short_16","alias_value":"6YVCX4NWBBVWFFG2","created_at":"2026-05-18T12:32:11Z"},{"alias_kind":"pith_short_8","alias_value":"6YVCX4NW","created_at":"2026-05-18T12:32:11Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2018:6YVCX4NWBBVWFFG2PR36YOHGME","target":"record","payload":{"canonical_record":{"source":{"id":"1810.10353","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2018-10-22T07:39:12Z","cross_cats_sorted":["cs.HC"],"title_canon_sha256":"45d845b65b5037083acb5fd163330eba64fa7bae28546803eb96a42bdec17fbf","abstract_canon_sha256":"1e3745d548afb1b299919689ba4e5ed733bbf705d66bd3b318d7b05fe0c793df"},"schema_version":"1.0"},"canonical_sha256":"f62a2bf1b6086b6294da7c77ec38e6610884772a974cd9e2a339cd8d8142b445","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T00:02:23.424397Z","signature_b64":"IfPaYTTFppSoy6FKMU7gx/HjwgdhLU8VECXvsilVaTI/M2EmUlEICrNGbVan5UQltJd8rrr9wVFlQvbm0BBkDQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"f62a2bf1b6086b6294da7c77ec38e6610884772a974cd9e2a339cd8d8142b445","last_reissued_at":"2026-05-18T00:02:23.423771Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T00:02:23.423771Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"1810.10353","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":"2026-05-18T00:02:23Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"kPMBVBG8PA+fgVx7ZdERRiRuD1EzQGhuoi7wQPsi5fLTtudJKcayETWOmPZ2LI+QmQ40YNPvnBtF2bW/mWB4Bw==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-06-03T04:42:40.210590Z"},"content_sha256":"b5bbb9749fff8e14424911eaf29f4c8ca94ad3e0843222b9a5802bae4ebfeff0","schema_version":"1.0","event_id":"sha256:b5bbb9749fff8e14424911eaf29f4c8ca94ad3e0843222b9a5802bae4ebfeff0"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2018:6YVCX4NWBBVWFFG2PR36YOHGME","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Boosted Convolutional Neural Networks for Motor Imagery EEG Decoding with Multiwavelet-based Time-Frequency Conditional Granger Causality Analysis","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.HC"],"primary_cat":"cs.CV","authors_text":"Hua-Liang Wei, Mengying Lei, Ting-Wen Huang, Weigang Cui, Xianrui Zhang, Yang Li, Yuzhu Guo","submitted_at":"2018-10-22T07:39:12Z","abstract_excerpt":"Decoding EEG signals of different mental states is a challenging task for brain-computer interfaces (BCIs) due to nonstationarity of perceptual decision processes. This paper presents a novel boosted convolutional neural networks (ConvNets) decoding scheme for motor imagery (MI) EEG signals assisted by the multiwavelet-based time-frequency (TF) causality analysis. Specifically, multiwavelet basis functions are first combined with Geweke spectral measure to obtain high-resolution TF-conditional Granger causality (CGC) representations, where a regularized orthogonal forward regression (ROFR) alg"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1810.10353","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"},"verdict_id":null},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-05-18T00:02:23Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"8dzXHIyI4AaRlQbKLFX4y4E475a0ebhi7dQPiz7MzvmbaIksi2olPWADQ7fkeO5KY+E1ngG63TWRpBWBhAv4BQ==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-06-03T04:42:40.211013Z"},"content_sha256":"0dc2568de50e8bb221cac333f80d1e2a9b8c620d518f70d4ca4e0854f9694c84","schema_version":"1.0","event_id":"sha256:0dc2568de50e8bb221cac333f80d1e2a9b8c620d518f70d4ca4e0854f9694c84"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/6YVCX4NWBBVWFFG2PR36YOHGME/bundle.json","state_url":"https://pith.science/pith/6YVCX4NWBBVWFFG2PR36YOHGME/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/6YVCX4NWBBVWFFG2PR36YOHGME/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":"2026-06-03T04:42:40Z","links":{"resolver":"https://pith.science/pith/6YVCX4NWBBVWFFG2PR36YOHGME","bundle":"https://pith.science/pith/6YVCX4NWBBVWFFG2PR36YOHGME/bundle.json","state":"https://pith.science/pith/6YVCX4NWBBVWFFG2PR36YOHGME/state.json","well_known_bundle":"https://pith.science/.well-known/pith/6YVCX4NWBBVWFFG2PR36YOHGME/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2018:6YVCX4NWBBVWFFG2PR36YOHGME","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":"1e3745d548afb1b299919689ba4e5ed733bbf705d66bd3b318d7b05fe0c793df","cross_cats_sorted":["cs.HC"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2018-10-22T07:39:12Z","title_canon_sha256":"45d845b65b5037083acb5fd163330eba64fa7bae28546803eb96a42bdec17fbf"},"schema_version":"1.0","source":{"id":"1810.10353","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1810.10353","created_at":"2026-05-18T00:02:23Z"},{"alias_kind":"arxiv_version","alias_value":"1810.10353v1","created_at":"2026-05-18T00:02:23Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1810.10353","created_at":"2026-05-18T00:02:23Z"},{"alias_kind":"pith_short_12","alias_value":"6YVCX4NWBBVW","created_at":"2026-05-18T12:32:11Z"},{"alias_kind":"pith_short_16","alias_value":"6YVCX4NWBBVWFFG2","created_at":"2026-05-18T12:32:11Z"},{"alias_kind":"pith_short_8","alias_value":"6YVCX4NW","created_at":"2026-05-18T12:32:11Z"}],"graph_snapshots":[{"event_id":"sha256:0dc2568de50e8bb221cac333f80d1e2a9b8c620d518f70d4ca4e0854f9694c84","target":"graph","created_at":"2026-05-18T00:02:23Z","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"},"paper":{"abstract_excerpt":"Decoding EEG signals of different mental states is a challenging task for brain-computer interfaces (BCIs) due to nonstationarity of perceptual decision processes. This paper presents a novel boosted convolutional neural networks (ConvNets) decoding scheme for motor imagery (MI) EEG signals assisted by the multiwavelet-based time-frequency (TF) causality analysis. Specifically, multiwavelet basis functions are first combined with Geweke spectral measure to obtain high-resolution TF-conditional Granger causality (CGC) representations, where a regularized orthogonal forward regression (ROFR) alg","authors_text":"Hua-Liang Wei, Mengying Lei, Ting-Wen Huang, Weigang Cui, Xianrui Zhang, Yang Li, Yuzhu Guo","cross_cats":["cs.HC"],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2018-10-22T07:39:12Z","title":"Boosted Convolutional Neural Networks for Motor Imagery EEG Decoding with Multiwavelet-based Time-Frequency Conditional Granger Causality Analysis"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1810.10353","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:b5bbb9749fff8e14424911eaf29f4c8ca94ad3e0843222b9a5802bae4ebfeff0","target":"record","created_at":"2026-05-18T00:02:23Z","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":"1e3745d548afb1b299919689ba4e5ed733bbf705d66bd3b318d7b05fe0c793df","cross_cats_sorted":["cs.HC"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2018-10-22T07:39:12Z","title_canon_sha256":"45d845b65b5037083acb5fd163330eba64fa7bae28546803eb96a42bdec17fbf"},"schema_version":"1.0","source":{"id":"1810.10353","kind":"arxiv","version":1}},"canonical_sha256":"f62a2bf1b6086b6294da7c77ec38e6610884772a974cd9e2a339cd8d8142b445","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"f62a2bf1b6086b6294da7c77ec38e6610884772a974cd9e2a339cd8d8142b445","first_computed_at":"2026-05-18T00:02:23.423771Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-05-18T00:02:23.423771Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"IfPaYTTFppSoy6FKMU7gx/HjwgdhLU8VECXvsilVaTI/M2EmUlEICrNGbVan5UQltJd8rrr9wVFlQvbm0BBkDQ==","signature_status":"signed_v1","signed_at":"2026-05-18T00:02:23.424397Z","signed_message":"canonical_sha256_bytes"},"source_id":"1810.10353","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:b5bbb9749fff8e14424911eaf29f4c8ca94ad3e0843222b9a5802bae4ebfeff0","sha256:0dc2568de50e8bb221cac333f80d1e2a9b8c620d518f70d4ca4e0854f9694c84"],"state_sha256":"630868504b5aa8c80d3451eb6e765986078d2d000bf4b14aa46968925f0b7d96"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"xf9GZ1vMotBed8JsYFR1gM4cJwGE6FXDsb+5nRq1S9REPlsmXNjeGlaEmvKlAoCvpERif14pNn2pJ7rFTgXZAQ==","signed_message":"bundle_sha256_bytes","signed_at":"2026-06-03T04:42:40.212962Z","bundle_sha256":"0eb8f3579b046304c9ca4655bcf7ffd838c21bb7e982c5d5990f8cd45defe129"}}