{"id":"W2079168863","doi":"10.1109/icassp.2013.6637797","title":"Separable common spatio-spectral pattern algorithm for classification of EEG signals","year":2013,"lang":"en","type":"article","venue":"","topic":"EEG and Brain-Computer Interfaces","field":"Neuroscience","cited_by":6,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"","keywords":"Computer science; Pattern recognition (psychology); Artificial intelligence; Brain–computer interface; Electroencephalography; Linear discriminant analysis; Separable space; Motor imagery; Feature extraction; Filter (signal processing); Spatial filter; Generalization; Ranking (information retrieval); Discriminant; Algorithm; Mathematics; Computer vision","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":[0.00009495371,0.00009863337,0.0001511988,0.00005421048,0.00006059761,0.00006752763,0.0002476556,0.00004400415,0.0004270311],"category_scores_gemma":[0.00002282453,0.00007886693,0.00006244484,0.00009382724,0.00005388481,0.0002394993,0.00003050575,0.00005789941,0.000104173],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001462375,"about_ca_system_score_gemma":0.0000123464,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001733379,"about_ca_topic_score_gemma":0.00001905991,"domain_scores_codex":[0.9990902,0.0000497482,0.0002564492,0.0002539615,0.0001471361,0.000202486],"domain_scores_gemma":[0.999278,0.0003017851,0.0001148264,0.0001978986,0.00006020782,0.00004725067],"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":[0.000002943965,0.0001132567,0.0008248883,0.00002030003,0.000004534175,3.469345e-7,0.0001413506,0.00004130896,0.7968082,0.0006400205,0.01035115,0.1910517],"study_design_scores_gemma":[0.000191827,0.0001406553,0.00542787,0.00001106447,0.00000317665,0.000002431815,0.00003373384,0.3007892,0.6907379,0.001476184,0.001095144,0.00009083327],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.6666922,0.00001161183,0.3269236,0.0008776684,0.0001958626,0.0005810179,0.0000270114,0.00008677919,0.004604257],"genre_scores_gemma":[0.9930322,0.000003600455,0.004553919,0.000612704,0.00004491302,0.00006434871,0.000006036092,0.00001086223,0.001671417],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.32634,"threshold_uncertainty_score":0.4675691,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04433916361778262,"score_gpt":0.2976662263700618,"score_spread":0.2533270627522792,"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."}}