{"id":"W1975319787","doi":"10.1155/2007/84386","title":"Towards Development of a 3-State Self-Paced Brain-Computer Interface","year":2007,"lang":"en","type":"article","venue":"Computational Intelligence and Neuroscience","topic":"EEG and Brain-Computer Interfaces","field":"Neuroscience","cited_by":47,"is_retracted":false,"has_abstract":true,"ca_institutions":"Terry Fox Research Institute; Neil Squire Society; University of British Columbia","funders":"","keywords":"Brain–computer interface; Computer science; Asynchronous communication; False positive paradox; Electroencephalography; Context (archaeology); State (computer science); Interface (matter); Movement (music); Idle; Resting state fMRI; Artificial intelligence; Speech recognition; Real-time computing; Pattern recognition (psychology); Psychology; Neuroscience; Algorithm; Operating system","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":[0.0006240177,0.0002602397,0.0002491669,0.0002775126,0.0002779751,0.0001307915,0.0006631065,0.00005006548,0.00001462714],"category_scores_gemma":[0.0002221511,0.0002309932,0.00006189611,0.0006981908,0.0004648149,0.0003692584,0.0003843837,0.0002086192,0.00002487291],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00003866572,"about_ca_system_score_gemma":0.0001463099,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000003911587,"about_ca_topic_score_gemma":0.000003849123,"domain_scores_codex":[0.997372,0.00007711695,0.0006749266,0.0007926226,0.0006084964,0.0004748877],"domain_scores_gemma":[0.9984087,0.0008348942,0.0002297321,0.0001917327,0.0001342029,0.0002007037],"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":[0.0001458445,0.0006404283,0.0005658386,0.0001538456,0.00001223072,0.00009027151,0.01513258,0.1372828,0.4634857,0.008659374,0.000410535,0.3734205],"study_design_scores_gemma":[0.0001140175,0.0002546558,0.004209603,0.00005751564,0.000002744581,0.0001243695,0.00006311294,0.1027144,0.8865927,0.002745192,0.00284121,0.0002804109],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.439353,0.00003060749,0.5594785,0.0002268005,0.0004035168,0.0001427234,0.000003671333,0.00008461253,0.0002765926],"genre_scores_gemma":[0.9602002,0.0000147631,0.03767978,0.001919735,0.00004207415,0.000003878465,7.263787e-7,0.00001473528,0.0001241209],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.5217987,"threshold_uncertainty_score":0.9419628,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05314017254030061,"score_gpt":0.3257933146467913,"score_spread":0.2726531421064907,"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."}}