{"id":"W2069524881","doi":"10.3389/fnins.2014.00431","title":"Temporal alignment of electrocorticographic recordings for upper limb movement","year":2015,"lang":"en","type":"article","venue":"Frontiers in Neuroscience","topic":"EEG and Brain-Computer Interfaces","field":"Neuroscience","cited_by":8,"is_retracted":false,"has_abstract":true,"ca_institutions":"Toronto Western Hospital; University Health Network; Toronto Rehabilitation Institute; University of Toronto","funders":"","keywords":"Computer science; Movement (music); Salience (neuroscience); Electrocorticography; Brain activity and meditation; Brain–computer interface; Artificial intelligence; Task (project management); Motor cortex; Primary motor cortex; Neural activity; Pattern recognition (psychology); Electroencephalography; Neuroscience; Psychology","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":[0.0004207268,0.0004024649,0.0002205319,0.0009512714,0.0001979149,0.0004312211,0.0002041788,0.0003349214,0.002150121],"category_scores_gemma":[0.002302129,0.0001486202,0.0002763178,0.00166681,0.0001833435,0.000399555,0.0002310114,0.0002824113,0.0006020011],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001501932,"about_ca_system_score_gemma":0.0002328921,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0007162163,"about_ca_topic_score_gemma":0.001900218,"domain_scores_codex":[0.9997151,0.00006704323,0.00002312488,0.00006835385,0.0001071758,0.00001915786],"domain_scores_gemma":[0.9995611,0.0001593759,0.00008436949,0.00006867452,0.0001025638,0.00002379792],"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":[0.0006282083,0.00007416849,0.006366258,0.0004143108,0.0001036597,0.0003258775,0.0002900715,0.003809108,0.6814502,0.001567409,0.00151769,0.3034531],"study_design_scores_gemma":[0.0001177792,0.0008102242,0.5038468,0.0001512517,0.0003740081,0.004876743,0.0004751819,0.128649,0.3291484,0.00666519,0.02475541,0.000130092],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.2517443,0.001829142,0.7346769,0.0001647601,0.0002973384,0.0003773623,0.001244725,0.001316662,0.008348838],"genre_scores_gemma":[0.5856043,0.001414803,0.4092209,0.00009114193,0.0001874843,0.0003651911,0.00110984,0.0003074321,0.001698968],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.002150121,"threshold_uncertainty_score":0.00719285,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03530353120542179,"score_gpt":0.272189383755181,"score_spread":0.2368858525497592,"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."}}