{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2018:CUVRJWH4DZ75FZRHMWEOJREGRK","short_pith_number":"pith:CUVRJWH4","canonical_record":{"source":{"id":"1811.02353","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"eess.SP","submitted_at":"2018-11-06T14:00:05Z","cross_cats_sorted":["cs.HC","cs.LG"],"title_canon_sha256":"5ec0cb4e08a21741ed074590d10e1750fa65aa689f08e08ae90f75938a93d572","abstract_canon_sha256":"70b49904c92914564f5660a75b7db2ef733ee8b6d123c0241db28258a99660f8"},"schema_version":"1.0"},"canonical_sha256":"152b14d8fc1e7fd2e6276588e4c4868aa375813b013b453268afef5088af1f9a","source":{"kind":"arxiv","id":"1811.02353","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1811.02353","created_at":"2026-05-18T00:01:24Z"},{"alias_kind":"arxiv_version","alias_value":"1811.02353v1","created_at":"2026-05-18T00:01:24Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1811.02353","created_at":"2026-05-18T00:01:24Z"},{"alias_kind":"pith_short_12","alias_value":"CUVRJWH4DZ75","created_at":"2026-05-18T12:32:19Z"},{"alias_kind":"pith_short_16","alias_value":"CUVRJWH4DZ75FZRH","created_at":"2026-05-18T12:32:19Z"},{"alias_kind":"pith_short_8","alias_value":"CUVRJWH4","created_at":"2026-05-18T12:32:19Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2018:CUVRJWH4DZ75FZRHMWEOJREGRK","target":"record","payload":{"canonical_record":{"source":{"id":"1811.02353","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"eess.SP","submitted_at":"2018-11-06T14:00:05Z","cross_cats_sorted":["cs.HC","cs.LG"],"title_canon_sha256":"5ec0cb4e08a21741ed074590d10e1750fa65aa689f08e08ae90f75938a93d572","abstract_canon_sha256":"70b49904c92914564f5660a75b7db2ef733ee8b6d123c0241db28258a99660f8"},"schema_version":"1.0"},"canonical_sha256":"152b14d8fc1e7fd2e6276588e4c4868aa375813b013b453268afef5088af1f9a","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T00:01:24.485439Z","signature_b64":"qEWu+964V/8o4PlCgwy1fiyyytc7DRrehfdSQRbqqubVV/Ije6zuxINTsh8QjKQlFwOakpYfvL2TFn1bHho5Cg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"152b14d8fc1e7fd2e6276588e4c4868aa375813b013b453268afef5088af1f9a","last_reissued_at":"2026-05-18T00:01:24.485032Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T00:01:24.485032Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"1811.02353","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":"2026-05-18T00:01:24Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"ktI7STMeCnJM0JVEAPrShkEJbWSZGGHvLpllk1luRw9iYYdWtbncbVqoUYEyY4CSS0v/klQGaJJBTJ2d0weWCg==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-06-03T13:55:47.991246Z"},"content_sha256":"e92751c1f1331f627d995a38e5042e92eb79490f974a2f253a253f519175b50c","schema_version":"1.0","event_id":"sha256:e92751c1f1331f627d995a38e5042e92eb79490f974a2f253a253f519175b50c"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2018:CUVRJWH4DZ75FZRHMWEOJREGRK","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"An amplitudes-perturbation data augmentation method in convolutional neural networks for EEG decoding","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.HC","cs.LG"],"primary_cat":"eess.SP","authors_text":"Meng-Ying Lei, Xian-Rui Zhang, Yang Li","submitted_at":"2018-11-06T14:00:05Z","abstract_excerpt":"Brain-Computer Interface (BCI) system provides a pathway between humans and the outside world by analyzing brain signals which contain potential neural information. Electroencephalography (EEG) is one of most commonly used brain signals and EEG recognition is an important part of BCI system. Recently, convolutional neural networks (ConvNet) in deep learning are becoming the new cutting edge tools to tackle the problem of EEG recognition. However, training an effective deep learning model requires a big number of data, which limits the application of EEG datasets with a small number of samples."},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1811.02353","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":"2026-05-18T00:01:24Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"oJNI1D3tipaZKSsIw52S8DSe4crdZICkz+3q0lIMV0uODsDY4SqqJl7awXUZCNKuGdBZClVtaewmUlwXDGV6BA==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-06-03T13:55:47.991616Z"},"content_sha256":"36acad5da216968d5eb868dfdc82ed4adc8912ff2c7f2488ab1fd21d2f1a591c","schema_version":"1.0","event_id":"sha256:36acad5da216968d5eb868dfdc82ed4adc8912ff2c7f2488ab1fd21d2f1a591c"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/CUVRJWH4DZ75FZRHMWEOJREGRK/bundle.json","state_url":"https://pith.science/pith/CUVRJWH4DZ75FZRHMWEOJREGRK/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/CUVRJWH4DZ75FZRHMWEOJREGRK/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":"2026-06-03T13:55:47Z","links":{"resolver":"https://pith.science/pith/CUVRJWH4DZ75FZRHMWEOJREGRK","bundle":"https://pith.science/pith/CUVRJWH4DZ75FZRHMWEOJREGRK/bundle.json","state":"https://pith.science/pith/CUVRJWH4DZ75FZRHMWEOJREGRK/state.json","well_known_bundle":"https://pith.science/.well-known/pith/CUVRJWH4DZ75FZRHMWEOJREGRK/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2018:CUVRJWH4DZ75FZRHMWEOJREGRK","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":"70b49904c92914564f5660a75b7db2ef733ee8b6d123c0241db28258a99660f8","cross_cats_sorted":["cs.HC","cs.LG"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"eess.SP","submitted_at":"2018-11-06T14:00:05Z","title_canon_sha256":"5ec0cb4e08a21741ed074590d10e1750fa65aa689f08e08ae90f75938a93d572"},"schema_version":"1.0","source":{"id":"1811.02353","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1811.02353","created_at":"2026-05-18T00:01:24Z"},{"alias_kind":"arxiv_version","alias_value":"1811.02353v1","created_at":"2026-05-18T00:01:24Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1811.02353","created_at":"2026-05-18T00:01:24Z"},{"alias_kind":"pith_short_12","alias_value":"CUVRJWH4DZ75","created_at":"2026-05-18T12:32:19Z"},{"alias_kind":"pith_short_16","alias_value":"CUVRJWH4DZ75FZRH","created_at":"2026-05-18T12:32:19Z"},{"alias_kind":"pith_short_8","alias_value":"CUVRJWH4","created_at":"2026-05-18T12:32:19Z"}],"graph_snapshots":[{"event_id":"sha256:36acad5da216968d5eb868dfdc82ed4adc8912ff2c7f2488ab1fd21d2f1a591c","target":"graph","created_at":"2026-05-18T00:01:24Z","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":"Brain-Computer Interface (BCI) system provides a pathway between humans and the outside world by analyzing brain signals which contain potential neural information. Electroencephalography (EEG) is one of most commonly used brain signals and EEG recognition is an important part of BCI system. Recently, convolutional neural networks (ConvNet) in deep learning are becoming the new cutting edge tools to tackle the problem of EEG recognition. However, training an effective deep learning model requires a big number of data, which limits the application of EEG datasets with a small number of samples.","authors_text":"Meng-Ying Lei, Xian-Rui Zhang, Yang Li","cross_cats":["cs.HC","cs.LG"],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"eess.SP","submitted_at":"2018-11-06T14:00:05Z","title":"An amplitudes-perturbation data augmentation method in convolutional neural networks for EEG decoding"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1811.02353","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:e92751c1f1331f627d995a38e5042e92eb79490f974a2f253a253f519175b50c","target":"record","created_at":"2026-05-18T00:01:24Z","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":"70b49904c92914564f5660a75b7db2ef733ee8b6d123c0241db28258a99660f8","cross_cats_sorted":["cs.HC","cs.LG"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"eess.SP","submitted_at":"2018-11-06T14:00:05Z","title_canon_sha256":"5ec0cb4e08a21741ed074590d10e1750fa65aa689f08e08ae90f75938a93d572"},"schema_version":"1.0","source":{"id":"1811.02353","kind":"arxiv","version":1}},"canonical_sha256":"152b14d8fc1e7fd2e6276588e4c4868aa375813b013b453268afef5088af1f9a","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"152b14d8fc1e7fd2e6276588e4c4868aa375813b013b453268afef5088af1f9a","first_computed_at":"2026-05-18T00:01:24.485032Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-05-18T00:01:24.485032Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"qEWu+964V/8o4PlCgwy1fiyyytc7DRrehfdSQRbqqubVV/Ije6zuxINTsh8QjKQlFwOakpYfvL2TFn1bHho5Cg==","signature_status":"signed_v1","signed_at":"2026-05-18T00:01:24.485439Z","signed_message":"canonical_sha256_bytes"},"source_id":"1811.02353","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:e92751c1f1331f627d995a38e5042e92eb79490f974a2f253a253f519175b50c","sha256:36acad5da216968d5eb868dfdc82ed4adc8912ff2c7f2488ab1fd21d2f1a591c"],"state_sha256":"58f0df9beb7f4661af9478b430dcd68989e9a7e38c1238ec435f8a12ddab774c"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"qr3ws+fomN735kAS758HckxUDX03ONCHlC9RG/v4dvae808NGiAQO8vqJ6+9O/atVoGg6iC5wtXl+xyTnVQLAA==","signed_message":"bundle_sha256_bytes","signed_at":"2026-06-03T13:55:47.993552Z","bundle_sha256":"96481c9cf8aafddddf7acda5d577d3e0d30f38ad6392107a8252635dafa252d2"}}