{"id":"W2914212446","doi":"10.3389/fnhum.2019.00024","title":"Evaluating If Children Can Use Simple Brain Computer Interfaces","year":2019,"lang":"en","type":"article","venue":"Frontiers in Human Neuroscience","topic":"EEG and Brain-Computer Interfaces","field":"Neuroscience","cited_by":67,"is_retracted":false,"has_abstract":true,"ca_institutions":"Hotchkiss Brain Institute; Alberta Children's Hospital; University of Calgary","funders":"Heart and Stroke Foundation of Canada","keywords":"Brain–computer interface; Electroencephalography; Motor imagery; Task (project management); Set (abstract data type); Sensorimotor rhythm; Computer science; Cognition; Population; Physical medicine and rehabilitation; Audiology; Psychology; Medicine; 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":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0005147597,0.0003398863,0.0003778583,0.0004004101,0.0003181962,0.0006050581,0.001596552,0.00007978233,0.00004831397],"category_scores_gemma":[0.0004326502,0.0003209962,0.0000863995,0.0007274334,0.0004509227,0.0008951622,0.0005829497,0.0004762863,0.00001200279],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00009573602,"about_ca_system_score_gemma":0.00005628366,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00007014851,"about_ca_topic_score_gemma":0.00001642005,"domain_scores_codex":[0.9961713,0.0004164995,0.0005032764,0.001436358,0.0006758278,0.0007967551],"domain_scores_gemma":[0.9986586,0.0002496429,0.0002153434,0.0006937861,0.00003390422,0.0001487045],"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":[0.00003726057,0.0002659562,0.3028737,0.00003496591,0.000003779258,0.00004590834,0.001962276,0.04385376,0.6213925,0.0006475062,0.01566776,0.01321466],"study_design_scores_gemma":[0.002330578,0.002355574,0.1635914,0.0002301904,0.00001273407,0.0001168162,0.0001248895,0.5974625,0.2242139,0.002678519,0.005283367,0.001599419],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9868423,0.00001277647,0.00854916,0.0004016635,0.003069444,0.0006416882,0.00002594434,0.0001669189,0.0002900858],"genre_scores_gemma":[0.9913484,0.000003770459,0.002603985,0.004453813,0.0001110817,0.00001278608,0.000003436566,0.00003584405,0.0014269],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.5536088,"threshold_uncertainty_score":0.9999242,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05213901387928774,"score_gpt":0.322835521763329,"score_spread":0.2706965078840413,"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."}}