{"id":"W4283727927","doi":"10.3390/biomedicines10071551","title":"Multi-Channel Vision Transformer for Epileptic Seizure Prediction","year":2022,"lang":"en","type":"article","venue":"Biomedicines","topic":"EEG and Brain-Computer Interfaces","field":"Neuroscience","cited_by":72,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of British Columbia","funders":"Natural Sciences and Engineering Research Council of Canada; Canadian Institutes of Health Research; National Institute on Aging; National Institutes of Health","keywords":"Electroencephalography; Epilepsy; Computer science; Artificial intelligence; Transformer; Pattern recognition (psychology); Speech recognition; Psychology; Neuroscience; Engineering; Voltage","routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003374791,0.0007634298,0.0004392042,0.0009495437,0.0001442406,0.0004433492,0.0005555035,0.0003827029,0.001484026],"category_scores_gemma":[0.001450948,0.000148955,0.0004779229,0.0006694162,0.0001696782,0.0005737134,0.0004052024,0.0005520728,0.0006671813],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002645396,"about_ca_system_score_gemma":0.0004331582,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004496076,"about_ca_topic_score_gemma":0.004987016,"domain_scores_codex":[0.9998322,0.00002454052,0.00001060809,0.00003921504,0.00006343819,0.00003004383],"domain_scores_gemma":[0.9997608,0.00009051123,0.00002603792,0.00002145906,0.00008185225,0.00001933867],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0005650204,0.0002076333,0.007621562,0.0001565295,0.00009073129,0.000319439,0.00004282492,0.08680762,0.03457175,0.002111278,0.007396728,0.8601089],"study_design_scores_gemma":[0.00001441876,0.0001021957,0.002983494,0.00001151243,0.00003536622,0.0003142933,0.00003371558,0.9751602,0.01761616,0.002066345,0.001646481,0.0000157715],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.07113551,0.001523189,0.9200252,0.0002273762,0.0001516601,0.00009266018,0.0004932969,0.003368219,0.002982765],"genre_scores_gemma":[0.8866432,0.0009333201,0.1088978,0.0001176812,0.00006310512,0.00004454849,0.0009018988,0.00008330384,0.002315048],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.004496076,"threshold_uncertainty_score":0.008939803,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03951420636753254,"score_gpt":0.3009733109029198,"score_spread":0.2614591045353873,"validation_status":"score_only:v0-immature-baseline","note":"Baseline scores from an immature model (maturity gate not passed). Scores rank; they never assert a category."}}