{"id":"W2508533112","doi":"10.1016/j.neuroimage.2016.08.044","title":"Complex patterns of spatially extended generators of epileptic activity: Comparison of source localization methods cMEM and 4-ExSo-MUSIC on high resolution EEG and MEG data","year":2016,"lang":"en","type":"article","venue":"NeuroImage","topic":"Blind Source Separation Techniques","field":"Computer Science","cited_by":53,"is_retracted":false,"has_abstract":false,"ca_institutions":"Concordia University; École de Technologie Supérieure; Montreal Neurological Institute and Hospital; McGill University","funders":"Natural Sciences and Engineering Research Council of Canada; Canadian Institutes of Health Research; Epilepsy Society; Savoy Foundation","keywords":"Electroencephalography; Magnetoencephalography; Neuroscience; High resolution; Computer science; Pattern recognition (psychology); Artificial intelligence; Psychology; Geography; Remote sensing","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":[],"consensus_categories":[],"category_scores_codex":[0.000652979,0.0001454046,0.0003658161,0.0001856779,0.00005056158,0.00003213771,0.0004781366,0.00006999053,0.00000843001],"category_scores_gemma":[0.0001700413,0.0001193979,0.00002576982,0.000205293,0.0001511669,0.0004398653,0.0005018041,0.0000882075,4.246378e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001327074,"about_ca_system_score_gemma":0.00003502155,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001234442,"about_ca_topic_score_gemma":0.00004003073,"domain_scores_codex":[0.9980015,0.0006786878,0.000418139,0.0004670848,0.0002996678,0.0001348885],"domain_scores_gemma":[0.9980206,0.0003574523,0.0004854759,0.0009581501,0.0001230926,0.00005518846],"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":[0.00006038663,0.0002495231,0.00491294,0.0001080263,0.00001953053,0.000001285812,0.0008291467,0.0002146857,0.700712,0.005632682,0.0003141457,0.2869456],"study_design_scores_gemma":[0.0007011616,0.00075451,0.1323562,0.0001060177,0.00003049938,0.000004679787,0.00001585077,0.3105655,0.5540729,0.0007096431,0.0004879831,0.0001951511],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.3660328,0.00001570467,0.6334885,0.0001797858,0.00003402313,0.0001433941,0.00002947475,0.0000470271,0.00002932481],"genre_scores_gemma":[0.9255279,0.00003031865,0.07428584,0.0001066682,0.00001248358,0.000003097545,0.00001161075,0.00001331407,0.00000881863],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.5594951,"threshold_uncertainty_score":0.4868904,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.09785730437799431,"score_gpt":0.3616407195003127,"score_spread":0.2637834151223184,"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."}}