{"id":"W1979315298","doi":"10.1016/j.jneumeth.2011.04.032","title":"Single trial classification of magnetoencephalographic recordings using Granger causality","year":2011,"lang":"en","type":"article","venue":"Journal of Neuroscience Methods","topic":"EEG and Brain-Computer Interfaces","field":"Neuroscience","cited_by":6,"is_retracted":false,"has_abstract":false,"ca_institutions":"Canada Research Chairs; Hospital for Sick Children; University of Toronto","funders":"Natural Sciences and Engineering Research Council of Canada; Fundação Bial","keywords":"Magnetoencephalography; Granger causality; Computer science; Autoregressive model; Artificial intelligence; Bivariate analysis; Statistical hypothesis testing; Neuroimaging; Machine learning; Pattern recognition (psychology); Statistics; Data mining; Electroencephalography; Psychology; Mathematics; Neuroscience","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001620736,0.0007533227,0.0007439396,0.001568991,0.0002993245,0.001361848,0.0003790949,0.0008159804,0.002860021],"category_scores_gemma":[0.009693712,0.0001948973,0.0005463689,0.001192469,0.0003087728,0.0008302646,0.0006807723,0.0007101034,0.0009412384],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001468645,"about_ca_system_score_gemma":0.0005093795,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0006374135,"about_ca_topic_score_gemma":0.00112869,"domain_scores_codex":[0.9995093,0.0001726707,0.00005374036,0.0001304928,0.00007087067,0.00006288406],"domain_scores_gemma":[0.9980099,0.001297318,0.0001382521,0.0002522463,0.0002212805,0.00008101807],"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":[0.005621525,0.0004828773,0.03388096,0.0004478826,0.000453476,0.0003600752,0.0003364252,0.01755969,0.1029156,0.003573886,0.002775872,0.8315917],"study_design_scores_gemma":[0.0004782997,0.00110873,0.1077379,0.0001579773,0.0004345901,0.001811182,0.0003114351,0.7978691,0.06262182,0.02263873,0.004656743,0.000173528],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.4746073,0.001010871,0.5162566,0.0002675425,0.0004964511,0.0005346941,0.001475869,0.002518994,0.002831561],"genre_scores_gemma":[0.9103595,0.0004802487,0.08540258,0.00005911371,0.0001007309,0.000226517,0.001322522,0.0002958506,0.001752947],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.002860021,"threshold_uncertainty_score":0.009567678,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.366519926880113,"score_gpt":0.4224853812097218,"score_spread":0.05596545432960887,"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."}}