{"id":"W3137788206","doi":"10.3390/s21062020","title":"Effect of a Brain–Computer Interface Based on Pedaling Motor Imagery on Cortical Excitability and Connectivity","year":2021,"lang":"en","type":"article","venue":"Sensors","topic":"EEG and Brain-Computer Interfaces","field":"Neuroscience","cited_by":26,"is_retracted":false,"has_abstract":true,"ca_institutions":"Toronto Metropolitan University","funders":"Fundação de Amparo à Pesquisa e Inovação do Espírito Santo; Coordenação de Aperfeiçoamento de Pessoal de Nível Superior","keywords":"Motor imagery; Brain–computer interface; SMA*; Supplementary motor area; Electroencephalography; Primary motor cortex; Rhythm; Neuroscience; Motor cortex; Psychology; Sensory system; Brain activity and meditation; Physical medicine and rehabilitation; Computer science; Medicine; Functional magnetic resonance imaging; Stimulation","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005213101,0.0002045834,0.0003391016,0.00007118064,0.00008372643,0.00006184467,0.0001193693,0.00007127105,0.00003815048],"category_scores_gemma":[0.002219858,0.0001659578,0.0001146382,0.0001464857,0.0002122922,0.00005883384,0.0001006723,0.0003069604,0.0000155221],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00003083522,"about_ca_system_score_gemma":0.00002752201,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000007426358,"about_ca_topic_score_gemma":0.000002332735,"domain_scores_codex":[0.9974486,0.001143916,0.0002343302,0.0006606905,0.0002572164,0.0002552185],"domain_scores_gemma":[0.9905437,0.008869948,0.00007271698,0.0003726332,0.00004260606,0.00009845291],"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":[0.001011237,0.0003975774,0.002800297,0.0003183695,0.00001328288,0.000174889,0.0004466906,0.00481016,0.9792468,0.0001821785,0.0001488576,0.01044964],"study_design_scores_gemma":[0.0005928698,0.001558248,0.006068697,0.0001177812,0.000008125628,0.00002230863,0.00001362125,0.1620696,0.8293039,0.000029772,0.00006995841,0.0001450713],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9965334,0.0000077334,0.001573623,0.0007587407,0.0004097817,0.000213046,0.00002052499,0.00007392956,0.0004092415],"genre_scores_gemma":[0.9987397,9.505563e-7,0.0002787747,0.0008069584,0.00007813684,0.000003801079,6.323633e-7,0.00001617023,0.00007491419],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.1572594,"threshold_uncertainty_score":0.6767563,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0169050385500774,"score_gpt":0.2915603649916605,"score_spread":0.2746553264415831,"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."}}