{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001314834,0.0005937919,0.0006189187,0.001866254,0.0003067328,0.0008387407,0.0002596618,0.0004894979,0.0006790597],"category_scores_gemma":[0.01590417,0.0001494345,0.0004071956,0.001241548,0.0004340194,0.001328078,0.0005718363,0.0004995458,0.0001267816],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003744853,"about_ca_system_score_gemma":0.0003798931,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001641917,"about_ca_topic_score_gemma":0.000973759,"domain_scores_codex":[0.9991997,0.0001880491,0.00007506661,0.0001304572,0.0003337434,0.00007302509],"domain_scores_gemma":[0.9854518,0.01143216,0.001353739,0.0006727095,0.0008470987,0.0002424471],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"observational","study_design_scores_codex":[0.001839693,0.0004804504,0.231552,0.0003290186,0.0003523589,0.001225542,0.0004121556,0.455387,0.04098959,0.00571233,0.001389039,0.2603307],"study_design_scores_gemma":[0.00001617805,0.0004693176,0.1189629,0.00002815745,0.00004623993,0.0006648603,0.0001078582,0.8697109,0.006452844,0.003015123,0.000485768,0.00003978311],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9290998,0.0006337175,0.06838495,0.0001730095,0.00001830682,0.00004466801,0.0002871342,0.0001423096,0.00121613],"genre_scores_gemma":[0.9921366,0.0001287255,0.007278471,0.000010313,0.0000152541,0.00001561221,0.0002340078,0.00001357484,0.0001674953],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.001866254,"threshold_uncertainty_score":0.006953597,"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."}}