{"id":"W2122149860","doi":"10.1109/tnsre.2003.810426","title":"A general framework for brain-computer interface design","year":2003,"lang":"en","type":"article","venue":"IEEE Transactions on Neural Systems and Rehabilitation Engineering","topic":"EEG and Brain-Computer Interfaces","field":"Neuroscience","cited_by":326,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of British Columbia; Nautilus Environmental; Neil Squire Society","funders":"","keywords":"Brain–computer interface; Computer science; Interface (matter); Human–computer interaction; Set (abstract data type); Vocabulary; Field (mathematics); Electroencephalography; 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":[0.0002378424,0.0002019584,0.0002029666,0.0001525743,0.0001567312,0.0001268043,0.00009539582,0.00009660538,0.000005434396],"category_scores_gemma":[0.0001148637,0.0001762637,0.0001017471,0.0001743368,0.00004492777,0.000189761,9.561061e-7,0.0001966057,0.000004081031],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00004026152,"about_ca_system_score_gemma":0.00001027168,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000004761783,"about_ca_topic_score_gemma":3.298102e-7,"domain_scores_codex":[0.9987465,0.0001516247,0.0002974096,0.0004142711,0.000133975,0.0002562733],"domain_scores_gemma":[0.9966227,0.003028407,0.00005079833,0.0001691736,0.00003544106,0.00009346505],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00002359278,0.00004493695,0.000001649782,0.0001320695,0.00000962889,9.414696e-7,0.0005766834,0.9304039,0.06257407,0.004384329,0.0001518216,0.001696381],"study_design_scores_gemma":[0.000448208,0.0008645706,0.00002417401,0.000177084,0.00001016509,0.0000605717,0.00007375371,0.9186027,0.07665237,0.0002428786,0.002554617,0.0002888732],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1663768,0.00004697893,0.8302342,0.0003580172,0.002220687,0.0005994158,0.00001086765,0.0001458428,0.000007156962],"genre_scores_gemma":[0.9425013,0.000003696419,0.05690452,0.0001321631,0.0000907811,0.0001584651,1.551015e-7,0.00003063098,0.0001783059],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.7761245,"threshold_uncertainty_score":0.7187827,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0220058344504662,"score_gpt":0.2627519410713375,"score_spread":0.2407461066208713,"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."}}