{"id":"W1969040087","doi":"10.1016/j.neuroimage.2014.07.037","title":"Removing artefacts from TMS-EEG recordings using independent component analysis: Importance for assessing prefrontal and motor cortex network properties","year":2014,"lang":"en","type":"article","venue":"NeuroImage","topic":"Transcranial Magnetic Stimulation Studies","field":"Neuroscience","cited_by":324,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Toronto; Centre for Addiction and Mental Health","funders":"National Health and Medical Research Council; National Medical Research Council; BrainsWay; Pfizer","keywords":"Prefrontal cortex; Independent component analysis; Component (thermodynamics); Electroencephalography; Neuroscience; Motor cortex; Component analysis; Psychology; Computer science; Artificial intelligence; Cognition; Physics","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":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0002687332,0.000280721,0.0004902268,0.0001174821,0.000521653,0.0003283855,0.0001949269,0.00006523535,0.00002285305],"category_scores_gemma":[0.0005279397,0.000262048,0.0001356377,0.000312373,0.0001606297,0.0004330382,0.0001142495,0.0001845038,0.000003169062],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00004503518,"about_ca_system_score_gemma":0.00002430654,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001424712,"about_ca_topic_score_gemma":0.00006903075,"domain_scores_codex":[0.9976239,0.0002065894,0.0004907088,0.000861199,0.0003769013,0.0004407073],"domain_scores_gemma":[0.998613,0.0005862204,0.0002861403,0.0003315937,0.00006572697,0.0001173216],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"observational","study_design_scores_codex":[0.0001195784,0.00003666543,0.02348226,0.00002488122,0.00003447542,0.000009572266,0.0002077131,0.001258236,0.9735028,0.00002476944,0.00002627127,0.001272752],"study_design_scores_gemma":[0.0008230544,0.00009696959,0.7868685,0.00004892608,0.0003128122,0.000007869712,0.00004345108,0.2094276,0.001483899,0.0002098614,0.0004094246,0.0002676339],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9731449,0.0001915814,0.02505689,0.0001194915,0.0004381897,0.0006148483,0.00003060538,0.000125011,0.0002784277],"genre_scores_gemma":[0.992873,0.00002178928,0.006249123,0.0005206224,0.0002012625,0.00002338741,0.000005325208,0.00004172723,0.00006375772],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.9720189,"threshold_uncertainty_score":0.9999832,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.06398512033368836,"score_gpt":0.2811937058158311,"score_spread":0.2172085854821428,"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."}}