{"id":"W2138843090","doi":"10.1117/12.801362","title":"Separating cognitive processes with principal components analysis of EEG time-frequency distributions","year":2008,"lang":"en","type":"article","venue":"Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE","topic":"EEG and Brain-Computer Interfaces","field":"Neuroscience","cited_by":51,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Victoria","funders":"","keywords":"Electroencephalography; Time–frequency analysis; Principal component analysis; Computer science; Cognition; Pattern recognition (psychology); Event-related potential; Speech recognition; Time domain; Frequency domain; Independent component analysis; Artificial intelligence; Psychology; Neuroscience","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":[0.001389473,0.001480815,0.0005069347,0.002356752,0.0003503621,0.001288864,0.0004032097,0.0003438909,0.00220332],"category_scores_gemma":[0.004759674,0.0002874509,0.001045557,0.002570957,0.0004005403,0.0009475418,0.0005612029,0.0008319257,0.000981724],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002702593,"about_ca_system_score_gemma":0.0008381092,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002996991,"about_ca_topic_score_gemma":0.002349234,"domain_scores_codex":[0.9994942,0.0001099999,0.00004983808,0.0001280461,0.0001582263,0.00005976612],"domain_scores_gemma":[0.9991829,0.0004307315,0.00007978617,0.0001034092,0.0001819771,0.00002114942],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.0003708047,0.0003675877,0.005913584,0.0004498646,0.0001824523,0.000196055,0.0008028087,0.01492945,0.1119308,0.007223091,0.002423838,0.8552096],"study_design_scores_gemma":[0.0001633269,0.0006969383,0.2060894,0.0001641004,0.0003992032,0.0008760836,0.001194225,0.6083674,0.09623756,0.06619625,0.01924784,0.0003677051],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.09087815,0.0003439733,0.9037002,0.0001417271,0.000061463,0.000441762,0.0006090387,0.001734653,0.00208914],"genre_scores_gemma":[0.3277284,0.0009152198,0.6676474,0.00003880805,0.00008703926,0.0006779106,0.001341285,0.0003281934,0.00123582],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.002996991,"threshold_uncertainty_score":0.00737083,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02104647824473906,"score_gpt":0.2531465900864774,"score_spread":0.2321001118417383,"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."}}