{"id":"W3133195712","doi":"10.3390/app11041579","title":"A Study of EEG Feature Complexity in Epileptic Seizure Prediction","year":2021,"lang":"en","type":"article","venue":"Applied Sciences","topic":"EEG and Brain-Computer Interfaces","field":"Neuroscience","cited_by":24,"is_retracted":false,"has_abstract":true,"ca_institutions":"Centre Hospitalier de l’Université de Montréal; Institut National de la Recherche Scientifique; Université TÉLUQ","funders":"","keywords":"Ictal; Electroencephalography; Pattern recognition (psychology); Epileptic seizure; Epilepsy; Artificial intelligence; Computer science; Feature (linguistics); Linear discriminant analysis; Machine learning; Psychology; Neuroscience","routes":{"ca_aff":true,"ca_fund":false,"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":[0.0003263729,0.0001010246,0.0001792553,0.0001007,0.0001651291,0.00007077503,0.0003949386,0.00004070855,0.0000340579],"category_scores_gemma":[0.00007578637,0.00008155025,0.00002477718,0.001087371,0.000364647,0.0001258337,0.0001490994,0.0001676068,0.000009382346],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001574942,"about_ca_system_score_gemma":0.000065849,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001834932,"about_ca_topic_score_gemma":0.0002132186,"domain_scores_codex":[0.9985329,0.0001064503,0.0002012071,0.0005179189,0.0004258256,0.000215699],"domain_scores_gemma":[0.9994708,0.0001908081,0.00008137352,0.0001944304,0.00002374788,0.00003879194],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.00003593692,0.001604478,0.02891114,0.00004086192,0.00000741201,0.0000850906,0.01131729,0.002215815,0.9357556,0.01514469,0.001428525,0.003453212],"study_design_scores_gemma":[0.001930112,0.001081789,0.2274488,0.00009017252,0.00001847865,0.00009435911,0.02072912,0.007033135,0.7309229,0.009490136,0.0007358135,0.0004251483],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9888993,0.00002244942,0.00006407301,0.0003185658,0.0002171351,0.0002396001,0.000008007943,0.00004024544,0.01019066],"genre_scores_gemma":[0.9991026,0.000002959853,0.0004672148,0.0002568472,0.00002703752,0.00001532571,7.563372e-7,0.000003557977,0.0001237257],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.2048327,"threshold_uncertainty_score":0.3325523,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0799152596622724,"score_gpt":0.3083128120528715,"score_spread":0.2283975523905991,"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."}}