{"id":"W2979563119","doi":"10.1155/2019/2503431","title":"A Comparison between BCI Simulation and Neurofeedback for Forward/Backward Navigation in Virtual Reality","year":2019,"lang":"en","type":"article","venue":"Computational Intelligence and Neuroscience","topic":"EEG and Brain-Computer Interfaces","field":"Neuroscience","cited_by":14,"is_retracted":false,"has_abstract":true,"ca_institutions":"Université de Montréal","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Brain–computer interface; Neurofeedback; Motor imagery; Computer science; Session (web analytics); Sensorimotor rhythm; Electroencephalography; Context (archaeology); Virtual reality; Support vector machine; Artificial intelligence; Psychology; Neuroscience","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":[0.0003032669,0.0001814614,0.00025027,0.0001503951,0.0001767626,0.0001859962,0.0002708072,0.00006182803,0.000003576689],"category_scores_gemma":[0.0004454257,0.0001766897,0.00003927017,0.0004116181,0.0002772882,0.0005853024,0.0001415829,0.0001847214,0.00001198902],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00002395582,"about_ca_system_score_gemma":0.00003366134,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001012595,"about_ca_topic_score_gemma":0.000002131963,"domain_scores_codex":[0.9980145,0.0001204282,0.0004477625,0.0007913929,0.000338436,0.0002874486],"domain_scores_gemma":[0.9971068,0.002417597,0.0001585072,0.0001382553,0.00007442831,0.0001044302],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00005307379,0.0000561976,0.03802079,0.00004777624,6.582648e-7,0.000001908775,0.0005384223,0.9392779,0.006352222,0.004741988,0.00003072127,0.01087837],"study_design_scores_gemma":[0.0001988429,0.0004446355,0.057006,0.00005274337,0.000003683659,0.000009117847,0.00006704992,0.9134904,0.01829718,0.00975706,0.0004782568,0.0001951041],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.7571595,0.00001610867,0.2415735,0.000394906,0.000280452,0.0004595878,0.00002636047,0.00003734466,0.00005230285],"genre_scores_gemma":[0.9987428,0.000008390994,0.000337686,0.0007551612,0.00004540199,0.00001240654,0.0000101618,0.00001125264,0.0000767627],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.2415833,"threshold_uncertainty_score":0.7205197,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1333234217647313,"score_gpt":0.4002020913099072,"score_spread":0.266878669545176,"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."}}