{"id":"W2064937918","doi":"10.1016/j.neunet.2008.06.012","title":"Variational Bayesian least squares: An application to brain–machine interface data","year":2008,"lang":"en","type":"article","venue":"Neural Networks","topic":"EEG and Brain-Computer Interfaces","field":"Neuroscience","cited_by":31,"is_retracted":false,"has_abstract":true,"ca_institutions":"Université de Montréal; York University","funders":"National Institute of Neurological Disorders and Stroke","keywords":"Overfitting; Computer science; Artificial intelligence; Neurophysiology; Bayesian probability; Brain–computer interface; Linear model; Machine learning; Linear regression; Pattern recognition (psychology); Artificial neural network; Electroencephalography","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":[0.003161936,0.0007430196,0.0009629683,0.0008764138,0.0004167105,0.000800482,0.001611023,0.001638982,0.001862355],"category_scores_gemma":[0.01137266,0.0008019999,0.0009238137,0.001156043,0.0007471278,0.0008433769,0.001707497,0.001754762,0.0005500742],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006894502,"about_ca_system_score_gemma":0.001548479,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.007911677,"about_ca_topic_score_gemma":0.008439292,"domain_scores_codex":[0.999036,0.0004706508,0.00004490155,0.0001395412,0.0002686004,0.00004036962],"domain_scores_gemma":[0.9973714,0.001973111,0.0001357835,0.0001412889,0.0003161934,0.00006219037],"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":[0.000115744,0.00008824554,0.0009835,0.0002123314,0.0001382344,0.0001173373,0.0001670535,0.7526263,0.009100566,0.02550589,0.003930374,0.2070144],"study_design_scores_gemma":[0.000005222457,0.000005754188,0.00008054433,0.000002481595,0.000001878681,0.00001200726,0.000002870781,0.9950548,0.000442054,0.003904584,0.0004821062,0.000005588912],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.002704417,0.00008510703,0.9964085,0.0001116608,0.00001235281,0.0000269478,0.00004197895,0.0004375246,0.000171467],"genre_scores_gemma":[0.07093558,0.0001539514,0.9271929,0.00008626741,0.00002711947,0.000145219,0.0002439684,0.000299914,0.0009150513],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.007911677,"threshold_uncertainty_score":0.01672214,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04205080426875784,"score_gpt":0.304301683152532,"score_spread":0.2622508788837742,"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."}}