{"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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002003182,0.0001236255,0.0001398416,0.0001665497,0.0003578415,0.00001952098,0.0002226979,0.00003493197,0.000102365],"category_scores_gemma":[0.00006190355,0.00009636566,0.00007218695,0.0002802681,0.00009352966,0.0001141285,0.00003674994,0.000108269,0.000009747189],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0000333004,"about_ca_system_score_gemma":0.000020792,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000008686216,"about_ca_topic_score_gemma":0.000001953028,"domain_scores_codex":[0.9988724,0.00005491284,0.0002131746,0.0003550686,0.0002717664,0.0002326619],"domain_scores_gemma":[0.9995502,0.0001639837,0.00005474487,0.0001404822,0.0000246176,0.0000659474],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"not_applicable","study_design_scores_codex":[0.0001129216,0.0002462367,0.00005263181,0.00007250744,0.000008202782,0.00000995095,0.001693446,0.0002723796,0.9575282,0.00004114704,0.03089589,0.009066472],"study_design_scores_gemma":[0.005496121,0.004834821,0.002049681,0.0001159912,0.00006664298,0.0002605987,0.001593492,0.190392,0.2874539,0.0006191804,0.5066301,0.0004875042],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.823427,0.0006514798,0.1451852,0.0143118,0.01168561,0.002445576,0.0009675092,0.0007532274,0.0005725962],"genre_scores_gemma":[0.9960379,0.00001908153,0.0003748232,0.001433597,0.0003624274,0.0001579051,0.00002812749,0.00001922486,0.0015669],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.6700743,"threshold_uncertainty_score":0.3929677,"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."}}