{"id":"W2343665847","doi":"10.1109/tnsre.2016.2523565","title":"Endogenous sensory discrimination and selection by a fast brain switch for a high transfer rate brain-computer interface","year":2016,"lang":"en","type":"article","venue":"IEEE Transactions on Neural Systems and Rehabilitation Engineering","topic":"EEG and Brain-Computer Interfaces","field":"Neuroscience","cited_by":30,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Waterloo","funders":"China Scholarship Council","keywords":"Brain–computer interface; Computer science; Sensory system; Information transfer; Interface (matter); Class (philosophy); Task (project management); Selection (genetic algorithm); Transfer (computing); Electroencephalography; Human–computer interaction; Computer hardware; Artificial intelligence; Neuroscience; Psychology; Engineering; Telecommunications; Parallel computing","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":[0.0003984031,0.0004832916,0.0003758431,0.0003799253,0.000203267,0.0004395114,0.0007719047,0.000386727,0.004245899],"category_scores_gemma":[0.001383668,0.0001561007,0.0001688711,0.000269094,0.0002770509,0.0007217543,0.0004145215,0.0004992597,0.0008436169],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002827804,"about_ca_system_score_gemma":0.0002714122,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0004509479,"about_ca_topic_score_gemma":0.0007055622,"domain_scores_codex":[0.9996786,0.00005794005,0.00001955222,0.00008531169,0.0001261635,0.00003237916],"domain_scores_gemma":[0.9994605,0.0002625885,0.00006020447,0.00007263986,0.0001063725,0.00003767083],"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":[0.0006001357,0.000225102,0.00147083,0.0001815082,0.00004898994,0.0001782032,0.0001432364,0.002236596,0.7397692,0.002096142,0.001338807,0.2517112],"study_design_scores_gemma":[0.0003246312,0.002890275,0.01886564,0.00005511905,0.0002176791,0.003192309,0.0000969933,0.3328363,0.6197345,0.003604708,0.01805568,0.0001262401],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.3373574,0.0009566084,0.6527531,0.0002850042,0.0002246336,0.0003818935,0.000149426,0.002475863,0.005416112],"genre_scores_gemma":[0.8330628,0.0002285922,0.1633283,0.0001884394,0.00007802395,0.0001891475,0.00008855795,0.00008817964,0.002747748],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.004245899,"threshold_uncertainty_score":0.01420397,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01536848896346048,"score_gpt":0.2303788181732764,"score_spread":0.215010329209816,"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."}}