{"id":"W2155617375","doi":"10.1109/tbme.2010.2082540","title":"Regularized Common Spatial Pattern With Aggregation for EEG Classification in Small-Sample Setting","year":2010,"lang":"en","type":"article","venue":"IEEE Transactions on Biomedical Engineering","topic":"EEG and Brain-Computer Interfaces","field":"Neuroscience","cited_by":309,"is_retracted":false,"has_abstract":true,"ca_institutions":"Toronto Metropolitan University; University of Toronto","funders":"","keywords":"Pattern recognition (psychology); Regularization (linguistics); Computer science; Covariance matrix; Covariance; Artificial intelligence; Context (archaeology); Sample size determination; Algorithm; Data mining; Mathematics; Statistics","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":[0.000182489,0.0001499151,0.0001497465,0.0002322743,0.00009318536,0.00005205375,0.0001781033,0.0001273998,0.00001714884],"category_scores_gemma":[0.00006041794,0.00013155,0.00004994834,0.0002618036,0.00006583344,0.0001028843,0.000001448161,0.0004294153,0.000004009528],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00003662636,"about_ca_system_score_gemma":0.00002447462,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001200731,"about_ca_topic_score_gemma":0.0003043159,"domain_scores_codex":[0.9989483,0.0000256967,0.0002582959,0.0003294479,0.0001811433,0.0002571371],"domain_scores_gemma":[0.9989915,0.000645325,0.00005566474,0.0001901473,0.00001966552,0.00009772876],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00004888424,0.0001394764,0.0000255853,0.00004239162,0.000005732836,0.000003888062,0.0001829504,0.006338508,0.8156673,0.00005389638,0.000006240819,0.1774852],"study_design_scores_gemma":[0.0008209828,0.0001451101,0.0003897633,0.00008322833,0.000007235075,0.00001352776,0.00001215105,0.6361609,0.3614933,0.00003173472,0.0006934879,0.0001485788],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.3627668,6.233442e-7,0.6357272,0.0006187636,0.0005372448,0.0002103384,0.00002861212,0.0001061389,0.000004333242],"genre_scores_gemma":[0.9899808,0.00000220998,0.009653608,0.0001213732,0.00008286251,0.0001076799,0.00001007576,0.00002607335,0.00001527746],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.6298224,"threshold_uncertainty_score":0.5364453,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0185675103518127,"score_gpt":0.2443675976925153,"score_spread":0.2258000873407026,"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."}}