{"id":"W2112156373","doi":"10.1109/ner.2009.5109306","title":"A brain-computer interface based on mental tasks with a zero false activation rate","year":2009,"lang":"en","type":"article","venue":"","topic":"EEG and Brain-Computer Interfaces","field":"Neuroscience","cited_by":3,"is_retracted":false,"has_abstract":true,"ca_institutions":"Neil Squire Society; University of British Columbia","funders":"","keywords":"Autoregressive model; Brain–computer interface; Computer science; Interface (matter); Task (project management); False positive rate; Feature (linguistics); Artificial intelligence; Electroencephalography; Pattern recognition (psychology); Psychology; Mathematics; Statistics; Engineering; 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":[0.00332871,0.0007704947,0.0007915749,0.0007059389,0.0002673373,0.0009261632,0.0006174595,0.001066733,0.001494365],"category_scores_gemma":[0.01754338,0.0002629215,0.0003134458,0.0003311687,0.0007001337,0.00098602,0.0006413186,0.0004918198,0.0006454287],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002604364,"about_ca_system_score_gemma":0.0004328012,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0003320663,"about_ca_topic_score_gemma":0.0003637282,"domain_scores_codex":[0.9973338,0.001267868,0.0002096269,0.0002909214,0.0007892,0.0001086579],"domain_scores_gemma":[0.9924431,0.004857064,0.0006589527,0.0006813779,0.001222228,0.0001372152],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.006651889,0.0008296216,0.02298108,0.0009249467,0.0003079771,0.00107499,0.0006130032,0.0321933,0.3538615,0.006142295,0.002503493,0.5719159],"study_design_scores_gemma":[0.0004144987,0.003745854,0.05561631,0.00009494882,0.0002845444,0.006252042,0.000111526,0.6511309,0.2727886,0.006726217,0.00261626,0.0002183286],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.3241504,0.000333429,0.6702047,0.0001744636,0.00006014496,0.0003027616,0.00009309201,0.002213965,0.002466969],"genre_scores_gemma":[0.8278234,0.0001168106,0.1705149,0.0001114104,0.00002275134,0.0003565214,0.0001110941,0.00007057354,0.0008724623],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.00332871,"threshold_uncertainty_score":0.01760411,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01974733937791514,"score_gpt":0.2702479499197017,"score_spread":0.2505006105417866,"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."}}