{"id":"W2004824219","doi":"10.1007/s10439-006-9097-5","title":"User Customization of the Feature Generator of an Asynchronous Brain Interface","year":2006,"lang":"en","type":"article","venue":"Annals of Biomedical Engineering","topic":"EEG and Brain-Computer Interfaces","field":"Neuroscience","cited_by":25,"is_retracted":false,"has_abstract":false,"ca_institutions":"Neil Squire Society; University of British Columbia","funders":"","keywords":"Asynchronous communication; Personalization; Computer science; Generator (circuit theory); Interface (matter); Scheme (mathematics); Feature (linguistics); Brain–computer interface; Measure (data warehouse); Electroencephalography; Artificial intelligence; Human–computer interaction; Speech recognition; Real-time computing; Data mining; Neuroscience; Computer network; Mathematics; Operating system; Power (physics)","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":[0.0006121516,0.0006703926,0.000344681,0.0005727411,0.0002229914,0.0008153582,0.0008493104,0.0005931112,0.008289469],"category_scores_gemma":[0.005644349,0.000240162,0.0002633824,0.000179935,0.0001933857,0.0006164996,0.0004302533,0.0003938199,0.001122782],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001291029,"about_ca_system_score_gemma":0.0001403379,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0004867823,"about_ca_topic_score_gemma":0.0005200494,"domain_scores_codex":[0.9995863,0.0001156588,0.00003409171,0.0001058541,0.0001118149,0.0000463692],"domain_scores_gemma":[0.9976639,0.001531496,0.00007954296,0.0003694175,0.0002682812,0.00008731321],"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":[0.003095254,0.0004374173,0.005699092,0.0002250366,0.0001221969,0.001712716,0.001097695,0.01275823,0.4841937,0.002986916,0.003925379,0.4837464],"study_design_scores_gemma":[0.0003941744,0.001053842,0.02133516,0.00004916379,0.0002004797,0.004049643,0.0002005361,0.5628052,0.3922948,0.004058818,0.01339078,0.0001673215],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.2785639,0.0001227421,0.6975057,0.0001779251,0.0001472311,0.0002459926,0.0002123661,0.01688234,0.006141935],"genre_scores_gemma":[0.9407004,0.00003813894,0.05413023,0.0001660831,0.00004171856,0.0001126657,0.0001502206,0.001698728,0.002961843],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.008289469,"threshold_uncertainty_score":0.027731,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01418518764658789,"score_gpt":0.2640744740771709,"score_spread":0.2498892864305831,"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."}}