{"id":"W2150139296","doi":"10.1109/iembs.2005.1616438","title":"Neural Codes in Human Extracranial EEG: Identification of Epilepsy Features","year":2005,"lang":"en","type":"article","venue":"","topic":"EEG and Brain-Computer Interfaces","field":"Neuroscience","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto; Toronto Rehabilitation Institute; Toronto Western Hospital","funders":"Natural Sciences and Engineering Research Council of Canada; Canadian Institutes of Health Research","keywords":"Electroencephalography; Epilepsy; Computer science; Identification (biology); Artificial intelligence; Speech recognition; Pattern recognition (psychology); Neuroscience; Psychology; Biology","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":[0.0001285169,0.0001724704,0.0001017145,0.0003721157,0.00006984985,0.0001829223,0.0001001412,0.0001764005,0.0006470378],"category_scores_gemma":[0.001296338,0.00005620227,0.00008953168,0.0004296623,0.0001745898,0.0002881792,0.0001658959,0.0001828969,0.0001337867],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00008958105,"about_ca_system_score_gemma":0.00008928861,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0006831202,"about_ca_topic_score_gemma":0.001155297,"domain_scores_codex":[0.999941,0.000009972489,0.000004379495,0.00001280723,0.00002394682,0.000007802902],"domain_scores_gemma":[0.9998442,0.00006875891,0.00002372035,0.00001909002,0.00003543326,0.000008818288],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"observational","study_design_scores_codex":[0.0004611062,0.0001175345,0.01912347,0.0003845543,0.00004646264,0.0005657164,0.0002480209,0.02724941,0.4854276,0.003090824,0.0008378006,0.4624475],"study_design_scores_gemma":[0.00003534655,0.000360231,0.2750781,0.00004943084,0.00005007088,0.001749017,0.0001342741,0.5896165,0.1243158,0.005679449,0.002897151,0.00003452633],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9123302,0.000499944,0.08380319,0.0001150637,0.00003123854,0.00005312583,0.0003998961,0.0001772013,0.002590023],"genre_scores_gemma":[0.9810285,0.0001787099,0.01760479,0.00001184897,0.00000995413,0.00002201439,0.000291856,0.0000147198,0.0008375581],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.0006831202,"threshold_uncertainty_score":0.002164543,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02574918393287834,"score_gpt":0.3046132668196225,"score_spread":0.2788640828867441,"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."}}