{"id":"W3168113684","doi":"10.2174/1874120702115010001","title":"Feature Selection Techniques for the Analysis of Discriminative Features in Temporal and Frontal Lobe Epilepsy: A Comparative Study","year":2021,"lang":"en","type":"article","venue":"The Open Biomedical Engineering Journal","topic":"EEG and Brain-Computer Interfaces","field":"Neuroscience","cited_by":6,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Ottawa","funders":"","keywords":"Computer science; Epilepsy; Ictal; Artificial intelligence; Feature selection; Discriminative model; Pattern recognition (psychology); Temporal lobe; Frontal lobe; Feature (linguistics); Autoregressive model; Electroencephalography; Univariate; Machine learning; Statistics; Neuroscience; Mathematics; Psychology; Multivariate statistics","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":[0.0006477438,0.0001126834,0.000301503,0.0001538103,0.000155451,0.0001952872,0.0004637323,0.00004214845,0.00001185583],"category_scores_gemma":[0.0002134456,0.00005712685,0.00007235907,0.0007944967,0.0001144777,0.0001341358,0.0001988837,0.000430563,8.968792e-8],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00003446653,"about_ca_system_score_gemma":0.00004250748,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00004034926,"about_ca_topic_score_gemma":0.000103883,"domain_scores_codex":[0.9990883,0.0001576557,0.0001916793,0.0001837453,0.0002264814,0.0001521584],"domain_scores_gemma":[0.9990473,0.0006493256,0.00009917971,0.0001005611,0.00005141263,0.00005226366],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.001789274,0.004559195,0.01783602,0.0001754914,0.00551816,0.0004079177,0.1213078,0.01519718,0.733045,0.00401742,0.0263325,0.06981403],"study_design_scores_gemma":[0.003409486,0.002311506,0.2969889,0.0004590378,0.001365574,0.00104547,0.02061502,0.3882864,0.276207,0.0005299357,0.008088211,0.0006934829],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9441171,0.0004096286,0.04838894,0.005733429,0.0002986227,0.0009117552,0.00004235743,0.00002587763,0.00007233948],"genre_scores_gemma":[0.9973479,0.00002028777,0.002331791,0.00007843646,0.00007197271,0.00002577561,0.000002246692,0.000006012606,0.0001156157],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.456838,"threshold_uncertainty_score":0.2329565,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03185092686492987,"score_gpt":0.3320047458090641,"score_spread":0.3001538189441343,"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."}}