{"id":"W2773359360","doi":"10.1016/j.nicl.2017.12.005","title":"DeepIED: An epileptic discharge detector for EEG-fMRI based on deep learning","year":2017,"lang":"en","type":"article","venue":"NeuroImage Clinical","topic":"EEG and Brain-Computer Interfaces","field":"Neuroscience","cited_by":65,"is_retracted":false,"has_abstract":true,"ca_institutions":"McGill University; Montreal Neurological Institute and Hospital","funders":"Fonds de recherche du Québec – Nature et technologies; Uehara Memorial Foundation; Canadian Institutes of Health Research; Montreal Neurological Institute and Hospital; Japan Epilepsy Research Foundation; Canada Foundation for Innovation","keywords":"Ictal; Electroencephalography; Concordance; Scanner; Epilepsy; EEG-fMRI; Computer science; Artificial intelligence; Pattern recognition (psychology); Psychology; Medicine; Neuroscience","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":"codex-gemma-dda1882f352a","candidate_categories":["metaresearch","metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0009450979,0.0002974668,0.000431446,0.00007967643,0.0009934437,0.0005904555,0.001379542,0.0001603291,0.0001119978],"category_scores_gemma":[0.01326773,0.0002555296,0.0003421774,0.00005457693,0.0004341925,0.000443018,0.0002146554,0.0008637452,0.0002437698],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001517461,"about_ca_system_score_gemma":0.00004181473,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000003712229,"about_ca_topic_score_gemma":0.00001322182,"domain_scores_codex":[0.99655,0.0006177841,0.0006300905,0.001278278,0.0003403772,0.0005835152],"domain_scores_gemma":[0.9938503,0.004003187,0.0004069579,0.00135487,0.00005900129,0.0003256847],"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":[0.003503835,0.004793586,0.09830117,0.0002649059,0.00003658346,0.0007655522,0.0004424005,0.00263794,0.4649569,0.001905478,0.004294162,0.4180975],"study_design_scores_gemma":[0.004525808,0.007644935,0.178325,0.0001006714,0.0000565113,0.00003114785,0.00001962004,0.7138763,0.07090966,0.0005510505,0.02308185,0.000877422],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.981053,0.000005375778,0.01242257,0.001736798,0.002096314,0.0005373519,0.00002352012,0.0002770626,0.001848026],"genre_scores_gemma":[0.9925379,0.000008223044,0.001832047,0.004377217,0.0006275468,0.00004062629,0.000004548449,0.0000698829,0.0005019847],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.7112383,"threshold_uncertainty_score":0.9999897,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1081153760009256,"score_gpt":0.396712424026915,"score_spread":0.2885970480259895,"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."}}