{"id":"W3206296762","doi":"10.1016/j.bspc.2021.103205","title":"Improvement decoding performance based on GQDA during a high engagement demanding paradigm","year":2021,"lang":"en","type":"article","venue":"Biomedical Signal Processing and Control","topic":"EEG and Brain-Computer Interfaces","field":"Neuroscience","cited_by":2,"is_retracted":false,"has_abstract":false,"ca_institutions":"Toronto Metropolitan University","funders":"Natural Science Foundation of Hebei Province; China Postdoctoral Science Foundation; National Natural Science Foundation of China","keywords":"Computer science; Decoding methods; Discriminant; Linear discriminant analysis; Constraint (computer-aided design); Artificial intelligence; Generalization; Quadratic classifier; Electroencephalography; Pattern recognition (psychology); Quadratic equation; Margin (machine learning); Task (project management); Dimension (graph theory); Speech recognition; Machine learning; Support vector machine; Algorithm; Mathematics; 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":[0.0004265408,0.000653852,0.0003956419,0.0002431675,0.0001241349,0.0006128743,0.0002755569,0.0005448411,0.00424801],"category_scores_gemma":[0.005119059,0.00009355658,0.0001667542,0.0002153802,0.0001968431,0.0005635612,0.0004753765,0.0005667965,0.001006139],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001256796,"about_ca_system_score_gemma":0.0003470095,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001195175,"about_ca_topic_score_gemma":0.001343577,"domain_scores_codex":[0.9997001,0.00005619993,0.00003079358,0.00009080496,0.00008538157,0.00003685513],"domain_scores_gemma":[0.9992262,0.0004032976,0.00007315711,0.00007884164,0.0001540568,0.00006454818],"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":[0.005666271,0.0006680174,0.01159142,0.0005460049,0.0001717954,0.0002055103,0.0005970646,0.003173788,0.6833076,0.001083385,0.002349513,0.2906397],"study_design_scores_gemma":[0.000739167,0.009716603,0.4792019,0.0001668866,0.0006727965,0.002067789,0.0007211348,0.2041065,0.2836165,0.009006792,0.009766864,0.0002169315],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.945719,0.0004058841,0.04396643,0.0003109201,0.0003766841,0.000313895,0.001308902,0.0007255477,0.006872805],"genre_scores_gemma":[0.9851604,0.0001921169,0.01037702,0.0001713295,0.00006540825,0.0001315223,0.0007073003,0.0001254172,0.003069435],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.00424801,"threshold_uncertainty_score":0.01421106,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01758906931049717,"score_gpt":0.2468091983079082,"score_spread":0.229220128997411,"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."}}