{"id":"W1989492719","doi":"10.1016/j.neuroimage.2007.01.016","title":"Groupwise independent component decomposition of EEG data and partial least square analysis","year":2007,"lang":"en","type":"article","venue":"NeuroImage","topic":"EEG and Brain-Computer Interfaces","field":"Neuroscience","cited_by":68,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Toronto; Baycrest Hospital","funders":"Canadian Institutes of Health Research; James S. McDonnell Foundation","keywords":"Independent component analysis; Principal component analysis; Dimensionality reduction; Pattern recognition (psychology); Partial least squares regression; Computer science; Curse of dimensionality; Artificial intelligence; Redundancy (engineering); Component analysis; Electroencephalography; Exploratory data analysis; Data mining; Machine learning","routes":{"ca_aff":true,"ca_fund":true,"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":[0.0003371304,0.0001434973,0.0002367761,0.0002210339,0.00009514369,0.0000789702,0.0005069606,0.00004154189,0.00004037715],"category_scores_gemma":[0.00006664589,0.0001331729,0.00006613647,0.0003948401,0.0001370802,0.0002991892,0.0004753025,0.0001637019,0.00001161189],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001108202,"about_ca_system_score_gemma":0.000008608452,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00004245894,"about_ca_topic_score_gemma":0.00003988126,"domain_scores_codex":[0.9983096,0.0001274456,0.000345764,0.0006116426,0.0003556632,0.0002499254],"domain_scores_gemma":[0.9988242,0.0002485568,0.0001495785,0.0006408599,0.0000278193,0.0001089277],"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":[0.0001148175,0.0002649587,0.0129422,0.00003315016,0.00003039275,0.0001549646,0.0002664187,0.0001519519,0.979744,0.0002153462,0.0002139082,0.005867963],"study_design_scores_gemma":[0.0008048862,0.0002687654,0.3683594,0.00002691723,0.0002296868,0.00009488749,0.00007834186,0.0587665,0.5694157,0.00005400332,0.00160862,0.0002922101],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9779733,0.00003437588,0.02081089,0.0001913746,0.0001864042,0.0001344705,0.0001229338,0.00005186963,0.0004943184],"genre_scores_gemma":[0.9991874,0.0000137986,0.0003429431,0.0003334883,0.00004952852,0.000001075036,0.00003458175,0.00001154897,0.00002569918],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.4103281,"threshold_uncertainty_score":0.5430635,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04626839168322982,"score_gpt":0.3235410880467755,"score_spread":0.2772726963635457,"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."}}