{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2021:LB2MEASXU3JDQSQGWJBBDTRWC2","short_pith_number":"pith:LB2MEASX","schema_version":"1.0","canonical_sha256":"5874c20257a6d2384a06b24211ce361682ec173f71aa33bf17dd79da77c71072","source":{"kind":"arxiv","id":"2106.09135","version":1},"attestation_state":"computed","paper":{"title":"EEG-GNN: Graph Neural Networks for Classification of Electroencephalogram (EEG) Signals","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["eess.SP"],"primary_cat":"cs.LG","authors_text":"Andac Demir, Deniz Erdogmus, Masaki Haruna, Toshiaki Koike-Akino, Ye Wang","submitted_at":"2021-06-16T21:19:12Z","abstract_excerpt":"Convolutional neural networks (CNN) have been frequently used to extract subject-invariant features from electroencephalogram (EEG) for classification tasks. This approach holds the underlying assumption that electrodes are equidistant analogous to pixels of an image and hence fails to explore/exploit the complex functional neural connectivity between different electrode sites. We overcome this limitation by tailoring the concepts of convolution and pooling applied to 2D grid-like inputs for the functional network of electrode sites. Furthermore, we develop various graph neural network (GNN) m"},"verification_status":{"content_addressed":true,"pith_receipt":true,"author_attested":false,"weak_author_claims":0,"strong_author_claims":0,"externally_anchored":false,"storage_verified":false,"citation_signatures":0,"replication_records":0,"graph_snapshot":true,"references_resolved":false,"formal_links_present":false},"canonical_record":{"source":{"id":"2106.09135","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2021-06-16T21:19:12Z","cross_cats_sorted":["eess.SP"],"title_canon_sha256":"41dfc62268d0c617b49cbdb2f77a41ec2b99213ece884c554f4c8631499c47f2","abstract_canon_sha256":"35f1adcd2202ff9d27718fa9abb09aca6c645aa8348f8237b6fcd0b4f2693ffb"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T02:50:12.691088Z","signature_b64":"6xAFdFoL3/Itm7TYoMfi/TY0e1zTs7We2PiObeeQ2I1+h0hCHF2E/YDI3ffd1e56jsPWlyEhhJbOb/cHKSeUAw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"5874c20257a6d2384a06b24211ce361682ec173f71aa33bf17dd79da77c71072","last_reissued_at":"2026-07-05T02:50:12.690665Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T02:50:12.690665Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"EEG-GNN: Graph Neural Networks for Classification of Electroencephalogram (EEG) Signals","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["eess.SP"],"primary_cat":"cs.LG","authors_text":"Andac Demir, Deniz Erdogmus, Masaki Haruna, Toshiaki Koike-Akino, Ye Wang","submitted_at":"2021-06-16T21:19:12Z","abstract_excerpt":"Convolutional neural networks (CNN) have been frequently used to extract subject-invariant features from electroencephalogram (EEG) for classification tasks. This approach holds the underlying assumption that electrodes are equidistant analogous to pixels of an image and hence fails to explore/exploit the complex functional neural connectivity between different electrode sites. We overcome this limitation by tailoring the concepts of convolution and pooling applied to 2D grid-like inputs for the functional network of electrode sites. Furthermore, we develop various graph neural network (GNN) m"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2106.09135","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/2106.09135/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"},"aliases":[{"alias_kind":"arxiv","alias_value":"2106.09135","created_at":"2026-07-05T02:50:12.690734+00:00"},{"alias_kind":"arxiv_version","alias_value":"2106.09135v1","created_at":"2026-07-05T02:50:12.690734+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2106.09135","created_at":"2026-07-05T02:50:12.690734+00:00"},{"alias_kind":"pith_short_12","alias_value":"LB2MEASXU3JD","created_at":"2026-07-05T02:50:12.690734+00:00"},{"alias_kind":"pith_short_16","alias_value":"LB2MEASXU3JDQSQG","created_at":"2026-07-05T02:50:12.690734+00:00"},{"alias_kind":"pith_short_8","alias_value":"LB2MEASX","created_at":"2026-07-05T02:50:12.690734+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":0,"internal_anchor_count":0,"sample":[]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/LB2MEASXU3JDQSQGWJBBDTRWC2","json":"https://pith.science/pith/LB2MEASXU3JDQSQGWJBBDTRWC2.json","graph_json":"https://pith.science/api/pith-number/LB2MEASXU3JDQSQGWJBBDTRWC2/graph.json","events_json":"https://pith.science/api/pith-number/LB2MEASXU3JDQSQGWJBBDTRWC2/events.json","paper":"https://pith.science/paper/LB2MEASX"},"agent_actions":{"view_html":"https://pith.science/pith/LB2MEASXU3JDQSQGWJBBDTRWC2","download_json":"https://pith.science/pith/LB2MEASXU3JDQSQGWJBBDTRWC2.json","view_paper":"https://pith.science/paper/LB2MEASX","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2106.09135&json=true","fetch_graph":"https://pith.science/api/pith-number/LB2MEASXU3JDQSQGWJBBDTRWC2/graph.json","fetch_events":"https://pith.science/api/pith-number/LB2MEASXU3JDQSQGWJBBDTRWC2/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/LB2MEASXU3JDQSQGWJBBDTRWC2/action/timestamp_anchor","attest_storage":"https://pith.science/pith/LB2MEASXU3JDQSQGWJBBDTRWC2/action/storage_attestation","attest_author":"https://pith.science/pith/LB2MEASXU3JDQSQGWJBBDTRWC2/action/author_attestation","sign_citation":"https://pith.science/pith/LB2MEASXU3JDQSQGWJBBDTRWC2/action/citation_signature","submit_replication":"https://pith.science/pith/LB2MEASXU3JDQSQGWJBBDTRWC2/action/replication_record"}},"created_at":"2026-07-05T02:50:12.690734+00:00","updated_at":"2026-07-05T02:50:12.690734+00:00"}