{"id":"W2079409187","doi":"10.1088/1741-2560/4/2/014","title":"Identification of arm movements using correlation of electrocorticographic spectral components and kinematic recordings","year":2007,"lang":"en","type":"article","venue":"Journal of Neural Engineering","topic":"EEG and Brain-Computer Interfaces","field":"Neuroscience","cited_by":27,"is_retracted":false,"has_abstract":true,"ca_institutions":"University Health Network; Toronto Rehabilitation Institute; University of Toronto","funders":"","keywords":"Kinematics; Electrocorticography; Computer science; Upper limb; Motor cortex; Artificial intelligence; Histogram; Pattern recognition (psychology); Electroencephalography; Computer vision; Physical medicine and rehabilitation; Neuroscience; Psychology; Medicine; Physics","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":[0.0004056745,0.0004031916,0.0003214972,0.001089204,0.000148864,0.0003966661,0.000176832,0.0003520909,0.0007111286],"category_scores_gemma":[0.002987619,0.0001529365,0.00020298,0.00070793,0.0002107763,0.0003563491,0.0002141194,0.0001687512,0.0004436769],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001002943,"about_ca_system_score_gemma":0.0001815629,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001036996,"about_ca_topic_score_gemma":0.001743029,"domain_scores_codex":[0.9996445,0.00008554113,0.00002526395,0.00008732134,0.0001182391,0.0000391894],"domain_scores_gemma":[0.9991637,0.0003809603,0.0001656599,0.00005336801,0.0002012365,0.00003492161],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"observational","study_design_scores_codex":[0.001266939,0.0002078303,0.09250592,0.0005137872,0.0001548442,0.001042245,0.0004303785,0.006281632,0.4637268,0.0005956558,0.0006292093,0.4326448],"study_design_scores_gemma":[0.00007204416,0.0008122688,0.7672735,0.00009433376,0.0001551286,0.003756484,0.0004335994,0.1340573,0.08965643,0.001192841,0.00242104,0.00007508932],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.7624003,0.001125547,0.231695,0.0001038993,0.00007955776,0.0001579415,0.0002885408,0.0005279217,0.003621187],"genre_scores_gemma":[0.9612862,0.0003433874,0.03745305,0.00002400043,0.00004039143,0.0000376187,0.0001601539,0.00002215439,0.0006331305],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.001089204,"threshold_uncertainty_score":0.002379,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02243091406406954,"score_gpt":0.2597776683511423,"score_spread":0.2373467542870727,"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."}}