{"id":"W2135552098","doi":"10.1109/cne.2005.1419563","title":"A New Design of the Asynchronous Brain Computer Interface Using the Knowledge of the Path of Features","year":2005,"lang":"en","type":"article","venue":"","topic":"EEG and Brain-Computer Interfaces","field":"Neuroscience","cited_by":14,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of British Columbia","funders":"","keywords":"Asynchronous communication; Brain–computer interface; Computer science; Interface (matter); Movement (music); Range (aeronautics); Human–computer interaction; Control (management); Movement control; Electroencephalography; Artificial intelligence; Computer network; Psychology; Neuroscience; Engineering; Physical medicine and rehabilitation","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":[0.0002953843,0.00015466,0.0002161307,0.00003427374,0.0001037054,0.00002976481,0.001306767,0.00005506305,0.00004173207],"category_scores_gemma":[0.0001043675,0.00006406446,0.0001508627,0.0003132738,0.0003158632,0.00008699477,0.0005191126,0.0001956719,0.000002807194],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00002600259,"about_ca_system_score_gemma":0.0001429526,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00008998403,"about_ca_topic_score_gemma":0.00004244886,"domain_scores_codex":[0.9985645,0.0004637603,0.0003347821,0.0002311648,0.0002128093,0.000193026],"domain_scores_gemma":[0.9981952,0.0008787658,0.0002604746,0.0005815185,0.00005490366,0.00002918239],"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":[0.00003730683,0.000176815,0.000203248,0.00004374207,0.00002425855,2.621297e-7,0.008703935,0.02749259,0.9215677,0.001807427,0.01801217,0.02193053],"study_design_scores_gemma":[0.0002507496,0.0001114925,0.001146494,0.0001587599,0.00001389601,0.00002821879,0.00010878,0.03970516,0.9573902,0.0002065188,0.0007949289,0.00008484355],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.6733631,0.0004187672,0.319948,0.003109261,0.0006775537,0.00063229,0.000008694651,0.00003141278,0.001810997],"genre_scores_gemma":[0.9898974,0.000003356267,0.008105975,0.0007265952,0.000112179,0.00000102442,3.047143e-8,0.00001222212,0.001141266],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.3165343,"threshold_uncertainty_score":0.2612473,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03616487286414331,"score_gpt":0.2897670856310616,"score_spread":0.2536022127669182,"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."}}