{"id":"W2898643234","doi":"10.1109/embc.2018.8512475","title":"Identification of Time-Varying Cortico-cortical and Cortico-Muscular Coherence during Motor Tasks with Multivariate Autoregressive Models","year":2018,"lang":"en","type":"article","venue":"","topic":"EEG and Brain-Computer Interfaces","field":"Neuroscience","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"Montreal Neurological Institute and Hospital; McGill University","funders":"","keywords":"Computer science; Coherence (philosophical gambling strategy); Autoregressive model; Artificial intelligence; Electroencephalography; Pattern recognition (psychology); Multivariate statistics; Kalman filter; Neuroimaging; Brain–computer interface; Speech recognition; Machine learning; Neuroscience; Psychology; Mathematics","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":[0.0006644451,0.0003711078,0.0002532747,0.0003398207,0.00007213104,0.0002625771,0.0002620134,0.0002846297,0.0003769287],"category_scores_gemma":[0.002260703,0.0002073696,0.0004051341,0.0004000032,0.0001624693,0.0003120771,0.0002322768,0.0003403393,0.00009789643],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001552707,"about_ca_system_score_gemma":0.0002639234,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003167367,"about_ca_topic_score_gemma":0.00507125,"domain_scores_codex":[0.9998267,0.00007353272,0.000008497722,0.00004888022,0.00002379802,0.00001855801],"domain_scores_gemma":[0.9995977,0.0002949541,0.00005510918,0.00002462436,0.00002056191,0.000007055279],"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":[0.0004207237,0.0001760087,0.01571247,0.0002640574,0.0003244921,0.0002150718,0.0004470063,0.6050826,0.1151969,0.004482144,0.0006073769,0.2570712],"study_design_scores_gemma":[0.000006072378,0.00005666056,0.01204164,0.000005926991,0.00002465356,0.00003710921,0.00001436567,0.9832857,0.003492567,0.0008775085,0.0001436629,0.00001417171],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.2653147,0.0002184673,0.7335767,0.00006828445,0.000010356,0.00003160144,0.0001076404,0.0003034667,0.000368858],"genre_scores_gemma":[0.9012208,0.0001972735,0.09785432,0.00001129851,0.00001303033,0.00007777339,0.0001536371,0.00003242337,0.0004395337],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.003167367,"threshold_uncertainty_score":0.006297827,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02251091099570585,"score_gpt":0.2686547129802551,"score_spread":0.2461438019845493,"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."}}