{"id":"W2769268658","doi":"10.1109/taslp.2017.2758164","title":"EEG Classification of Covert Speech Using Regularized Neural Networks","year":2017,"lang":"en","type":"article","venue":"IEEE/ACM Transactions on Audio Speech and Language Processing","topic":"EEG and Brain-Computer Interfaces","field":"Neuroscience","cited_by":120,"is_retracted":false,"has_abstract":true,"ca_institutions":"Holland Bloorview Kids Rehabilitation Hospital; University of Toronto","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Covert; Computer science; Electroencephalography; Speech recognition; Brain–computer interface; Task (project management); Artificial neural network; Motor imagery; Artificial intelligence; Binary classification; Pattern recognition (psychology); Psychology; Support vector machine","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":[0.0002078794,0.0002277798,0.0002887362,0.0001423055,0.0008683252,0.0004029926,0.0005370151,0.0001354867,0.00003925586],"category_scores_gemma":[0.0001193564,0.0002048315,0.00009347279,0.0001621909,0.0002784152,0.0005834503,0.00001614388,0.0003535475,0.000002917994],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00003698452,"about_ca_system_score_gemma":0.00004639854,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000101321,"about_ca_topic_score_gemma":0.00004081493,"domain_scores_codex":[0.998472,0.00008365784,0.0003262822,0.0005099336,0.0002855747,0.0003225365],"domain_scores_gemma":[0.9986762,0.0001219484,0.0003576468,0.0006754611,0.00006488752,0.0001037979],"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":[0.00009611662,0.00008662687,0.00007714302,0.00007253125,0.000008654538,0.0000323526,0.0005775772,0.001111601,0.6275607,0.0000046462,0.00001148489,0.3703606],"study_design_scores_gemma":[0.0007348894,0.00009224482,0.000812092,0.0002199059,0.00004799971,0.0001878217,0.0002983182,0.3157578,0.6814507,0.00008876964,0.00004926208,0.000260102],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8055257,0.0002148616,0.1927469,0.0003498202,0.00040625,0.0002005864,0.00001551922,0.00009527989,0.0004450277],"genre_scores_gemma":[0.9902405,0.00003424183,0.008869887,0.00024426,0.0001075504,0.000005820326,0.00000158196,0.00002966738,0.0004664855],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.3701005,"threshold_uncertainty_score":0.8352786,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04202514165730731,"score_gpt":0.3117690161908029,"score_spread":0.2697438745334956,"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."}}