{"id":"W2970967890","doi":"10.1515/bmt-2019-0001","title":"Epileptic seizure detection on EEG signals using machine learning techniques and advanced preprocessing methods","year":2019,"lang":"en","type":"article","venue":"Biomedizinische Technik/Biomedical Engineering","topic":"EEG and Brain-Computer Interfaces","field":"Neuroscience","cited_by":56,"is_retracted":false,"has_abstract":true,"ca_institutions":"Université de Montréal","funders":"","keywords":"Electroencephalography; Computer science; Support vector machine; Preprocessor; Artificial intelligence; Pattern recognition (psychology); Feature extraction; Epileptic seizure; Binary classification; Linear discriminant analysis; Sensitivity (control systems); Speech recognition; Machine learning; Psychology","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":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0009672373,0.0004473015,0.0004927406,0.0007159048,0.0001900027,0.0001295865,0.0004013754,0.0003565954,0.0000446221],"category_scores_gemma":[0.0007178957,0.0003914612,0.0001004571,0.00102002,0.00019823,0.0003476311,0.0003005527,0.0009322863,0.00001904946],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00008808479,"about_ca_system_score_gemma":0.00003624634,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000007894005,"about_ca_topic_score_gemma":1.830124e-7,"domain_scores_codex":[0.997183,0.0001548774,0.0005326659,0.000957923,0.0005482467,0.000623246],"domain_scores_gemma":[0.998418,0.0007138147,0.0002003787,0.0003570001,0.00003839098,0.0002724555],"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":[0.00003182413,0.00004886533,0.00001503656,0.0001988007,0.00001125179,0.00002530564,0.00009679498,0.0003562327,0.8795866,0.00004823735,0.000003310074,0.1195777],"study_design_scores_gemma":[0.0003641445,0.0005058692,0.00001965362,0.0004573282,0.00001571399,0.0002328155,0.00003043082,0.1969509,0.7743713,0.00003811527,0.02663542,0.0003783318],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.6886072,0.0004733241,0.3063684,0.0002015156,0.0007217403,0.0007483831,0.00001179754,0.00251582,0.0003517994],"genre_scores_gemma":[0.8757792,0.00005433819,0.1236173,0.0001350468,0.0001780592,0.00004271204,0.000004822203,0.00008553884,0.0001029996],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.1965947,"threshold_uncertainty_score":0.9998537,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01985292813912154,"score_gpt":0.3130572486743057,"score_spread":0.2932043205351841,"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."}}