{"id":"W2339186317","doi":"10.1371/journal.pone.0129435","title":"Comparing Different Classifiers in Sensory Motor Brain Computer Interfaces","year":2015,"lang":"en","type":"article","venue":"PLoS ONE","topic":"EEG and Brain-Computer Interfaces","field":"Neuroscience","cited_by":70,"is_retracted":false,"has_abstract":true,"ca_institutions":"BC Cancer Agency; Neil Squire Society; University of British Columbia","funders":"","keywords":"Brain–computer interface; Computer science; Linear discriminant analysis; Classifier (UML); Motor imagery; Artificial intelligence; Quadratic classifier; Interface (matter); Machine learning; Feature extraction; Pattern recognition (psychology); Statistical classification; Field (mathematics); Discriminant; Electroencephalography; Mathematics","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001703301,0.000210221,0.0003783121,0.0001732534,0.00004752759,0.000130525,0.0004348602,0.00006938564,0.00002039437],"category_scores_gemma":[0.0001581727,0.0001818179,0.00004254664,0.0001359343,0.0001207416,0.0001952311,0.0002787489,0.0003281369,0.0001389755],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001072717,"about_ca_system_score_gemma":0.00002186543,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00002158489,"about_ca_topic_score_gemma":0.00003934489,"domain_scores_codex":[0.9981635,0.0002328545,0.0003164103,0.0004835714,0.000422733,0.000380943],"domain_scores_gemma":[0.9990535,0.000365641,0.00008636401,0.0002797927,0.00003361173,0.000181119],"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":[0.0001027888,0.001715244,0.02395692,0.00008385719,0.00003419301,0.00005086898,0.002469136,0.0002215469,0.9684465,0.0002815303,0.001707081,0.0009303318],"study_design_scores_gemma":[0.001070029,0.000356036,0.005377417,0.0003253557,0.00001137706,0.000007741846,0.0001582806,0.130074,0.8618419,0.0002477078,0.0002122006,0.0003179234],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9958476,0.00003834156,0.0005007064,0.001403362,0.0003043849,0.0002536165,0.000007271639,0.0001625723,0.001482189],"genre_scores_gemma":[0.9962877,0.000004922244,0.00104024,0.001229749,0.0001807874,0.0000134588,0.00000143354,0.00002208167,0.001219618],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.1298524,"threshold_uncertainty_score":0.741432,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1953465871391307,"score_gpt":0.2799159354270411,"score_spread":0.08456934828791038,"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."}}