{"id":"W4212881677","doi":"10.1152/jn.00368.2021","title":"A machine learning approach to characterize sequential movement-related states in premotor and motor cortices","year":2022,"lang":"en","type":"article","venue":"Journal of Neurophysiology","topic":"EEG and Brain-Computer Interfaces","field":"Neuroscience","cited_by":6,"is_retracted":false,"has_abstract":true,"ca_institutions":"Centre for Interdisciplinary Research in Rehabilitation; Université de Montréal","funders":"Fonds de Recherche du Québec - Santé; Canadian Institutes of Health Research; Ministerio de Asuntos Económicos y Transformación Digital, Gobierno de España; Government of Canada","keywords":"Local field potential; Computer science; Neural decoding; Movement (music); Premotor cortex; Brain–computer interface; Neuroscience; Artificial intelligence; Decoding methods; Neuroprosthetics; Neural activity; Electroencephalography; Psychology; Dorsum","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":[0.0001053827,0.0001260603,0.000283181,0.0002331005,0.0001223737,0.00003803521,0.0003172678,0.00002286256,0.00004899914],"category_scores_gemma":[0.000128864,0.0001061885,0.00005591929,0.000205435,0.00005620128,0.00013967,0.0003397874,0.000603885,0.000002588461],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00003019359,"about_ca_system_score_gemma":0.00002385194,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001658376,"about_ca_topic_score_gemma":2.495473e-7,"domain_scores_codex":[0.9982714,0.0005908906,0.0004434647,0.0002693503,0.0001952532,0.0002296409],"domain_scores_gemma":[0.9992945,0.0001708217,0.0003395947,0.00008529347,0.00002397863,0.00008578486],"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":[0.0003939194,0.0001764985,0.0001624385,0.00001373316,0.000008688148,0.0001274999,0.0007740823,0.01493481,0.9826096,0.00004096479,0.00001203768,0.000745696],"study_design_scores_gemma":[0.008584189,0.03206185,0.2609725,0.0001118861,0.000090181,0.003417724,0.000868218,0.5652901,0.1032339,0.002476097,0.0215693,0.001324044],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9988534,0.00002645462,0.00003981471,0.00034528,0.0004767406,0.0001589532,0.00001640209,0.0000149301,0.00006798482],"genre_scores_gemma":[0.9977632,0.00005985334,0.0001482777,0.001737174,0.00006266047,0.000006534575,0.000002025876,0.00001576513,0.0002045772],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.8793758,"threshold_uncertainty_score":0.4330242,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02660779584658795,"score_gpt":0.2548688791370602,"score_spread":0.2282610832904723,"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."}}