{"id":"W2218893359","doi":"10.1109/eusipco.2015.7362818","title":"Feasibility analysis and adaptive thresholding for mobile applications controlled by EEG signals","year":2015,"lang":"en","type":"article","venue":"","topic":"EEG and Brain-Computer Interfaces","field":"Neuroscience","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"BC Research (Canada); University of British Columbia","funders":"National Research Foundation Singapore","keywords":"Electroencephalography; Thresholding; Computer science; Artificial intelligence; Brain–computer interface; Speech recognition; Channel (broadcasting); Classifier (UML); Pattern recognition (psychology); Telecommunications; Neuroscience; 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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006026634,0.0003164985,0.0002596014,0.0005093245,0.0001886663,0.0004475641,0.0004719799,0.0004072608,0.00118098],"category_scores_gemma":[0.003533577,0.0001730082,0.000253746,0.000291066,0.00034676,0.0006157045,0.0003115634,0.0002642215,0.0002255051],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002017917,"about_ca_system_score_gemma":0.000212767,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0005201921,"about_ca_topic_score_gemma":0.0005118984,"domain_scores_codex":[0.999552,0.0001040557,0.00002648425,0.0001009518,0.0001727451,0.00004383567],"domain_scores_gemma":[0.9987447,0.0007025404,0.000114172,0.00008148298,0.0003285371,0.00002858865],"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":[0.000881857,0.0001539438,0.006159708,0.0003195855,0.00005546407,0.0007149646,0.0003000202,0.03751025,0.6325981,0.005045914,0.0005316156,0.3157286],"study_design_scores_gemma":[0.00003591928,0.0009903259,0.02122129,0.00003745676,0.0000687389,0.0007333912,0.0002197376,0.7660413,0.2037075,0.004497225,0.002407864,0.00003921087],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.2027048,0.0002873735,0.7945992,0.0001662102,0.00002898544,0.0001096824,0.00002813783,0.0003994,0.001676152],"genre_scores_gemma":[0.9008612,0.000142063,0.098045,0.00002487766,0.00002087381,0.000072987,0.00003384252,0.00003890669,0.0007602958],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.00118098,"threshold_uncertainty_score":0.003950775,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.06984989653199562,"score_gpt":0.3342194747587364,"score_spread":0.2643695782267407,"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."}}