{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001060589,0.0004403178,0.0004613516,0.0003878353,0.0001828946,0.000860257,0.001289643,0.0008018658,0.002329998],"category_scores_gemma":[0.001657577,0.0003223292,0.0005881234,0.0002680141,0.0003877972,0.001074962,0.0006343431,0.0007430306,0.001270552],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004364617,"about_ca_system_score_gemma":0.0008464826,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001350184,"about_ca_topic_score_gemma":0.001150037,"domain_scores_codex":[0.9993647,0.0001210932,0.0000570608,0.0001547412,0.0002726791,0.00002980231],"domain_scores_gemma":[0.9990459,0.0001711029,0.00007699763,0.000101072,0.0005406438,0.0000641771],"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":[0.0004101268,0.0004311143,0.003500788,0.000692084,0.0001659923,0.0003276915,0.0007480523,0.0408112,0.2660738,0.04163702,0.005881949,0.6393201],"study_design_scores_gemma":[0.0001126693,0.00204272,0.0055165,0.0001765661,0.0001616148,0.0009014874,0.0001137858,0.7914678,0.1203458,0.01407158,0.06495775,0.0001316816],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.01155334,0.000261664,0.9841086,0.0001108422,0.00004370738,0.0001623475,0.00005044294,0.001505107,0.002203848],"genre_scores_gemma":[0.134299,0.0004980049,0.8608608,0.0002014306,0.0000395708,0.0003224989,0.0003162628,0.0001303531,0.003332091],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.002329998,"threshold_uncertainty_score":0.007794619,"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."}}