{"id":"W2061984316","doi":"10.1109/biorob.2012.6290944","title":"Brain-computer interface speller using hybrid P300 and motor imagery signals","year":2012,"lang":"en","type":"article","venue":"","topic":"EEG and Brain-Computer Interfaces","field":"Neuroscience","cited_by":10,"is_retracted":false,"has_abstract":true,"ca_institutions":"Carleton University","funders":"","keywords":"Brain–computer interface; Motor imagery; Computer science; Electroencephalography; Speech recognition; Mahalanobis distance; Interface (matter); Artificial intelligence; Pattern recognition (psychology); Neuroscience; Psychology","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":[0.0003065565,0.0002628781,0.0002578542,0.0001375702,0.0001255023,0.0002389071,0.0002966234,0.00004978611,0.0006057333],"category_scores_gemma":[0.00008119785,0.0002099424,0.0000831226,0.0001277546,0.0001621363,0.0007925187,0.0003938577,0.0001900822,0.0002971057],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0000290201,"about_ca_system_score_gemma":0.00001693481,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001845921,"about_ca_topic_score_gemma":4.569119e-7,"domain_scores_codex":[0.9982269,0.0001519456,0.0003020579,0.0004873132,0.0002309035,0.0006008301],"domain_scores_gemma":[0.9987583,0.0006064422,0.00009243148,0.000284853,0.00002782333,0.0002301958],"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":[0.0000206401,0.00009635298,0.000594431,0.00002602094,0.000009711431,0.00001496368,0.0004453341,0.00006280327,0.9760644,0.0001639803,0.01729295,0.005208395],"study_design_scores_gemma":[0.000288871,0.0001005708,0.0006243047,0.00004800794,0.000009088832,0.0003540702,0.00003283113,0.03382184,0.9515207,0.0001250614,0.01271427,0.0003603741],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9283513,0.0001965706,0.06613004,0.0007998641,0.0009904477,0.0002192195,0.00001111275,0.0001484312,0.003152987],"genre_scores_gemma":[0.9841279,0.000008154774,0.007230433,0.004725463,0.0006248695,0.000002027562,4.494466e-7,0.00003198473,0.003248703],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.05889961,"threshold_uncertainty_score":0.8561205,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0425317882814907,"score_gpt":0.29285730244956,"score_spread":0.2503255141680694,"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."}}