{"id":"W3138507454","doi":"10.3390/brainsci11040409","title":"Use of Empirical Mode Decomposition in ERP Analysis to Classify Familial Risk and Diagnostic Outcomes for Autism Spectrum Disorder","year":2021,"lang":"en","type":"article","venue":"Brain Sciences","topic":"Autism Spectrum Disorder Research","field":"Neuroscience","cited_by":17,"is_retracted":false,"has_abstract":true,"ca_institutions":"Brock University; McGill University; Montreal Neurological Institute and Hospital","funders":"Fonds de recherche du Québec – Nature et technologies","keywords":"Autism spectrum disorder; Hilbert–Huang transform; Support vector machine; Artificial intelligence; Psychology; Event-related potential; Electroencephalography; Outcome (game theory); Audiology; Machine learning; Autism; Pattern recognition (psychology); Developmental psychology; Computer science; Medicine; Neuroscience; Mathematics","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":[0.001882426,0.0004632474,0.0002552747,0.0007888381,0.0001005992,0.0003850115,0.0001994505,0.0002988412,0.0006328024],"category_scores_gemma":[0.005850374,0.0001007866,0.0003138474,0.000373414,0.0001691554,0.0003359975,0.0003037326,0.0003813968,0.0001868185],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001204469,"about_ca_system_score_gemma":0.0001970067,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0007942001,"about_ca_topic_score_gemma":0.0009288152,"domain_scores_codex":[0.9996828,0.0001484317,0.00002508679,0.00007550867,0.00004613582,0.00002200539],"domain_scores_gemma":[0.9985795,0.0008719525,0.0001652906,0.0001396551,0.0001909795,0.00005260156],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"observational","study_design_scores_codex":[0.001185524,0.0004728957,0.2508057,0.0002119211,0.0003040303,0.0004704097,0.000838863,0.02468348,0.1435204,0.001791048,0.000922389,0.5747934],"study_design_scores_gemma":[0.00003594362,0.0006306073,0.5515333,0.00007677872,0.000126922,0.00103223,0.000348373,0.418769,0.02221344,0.00409468,0.001071323,0.0000674065],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.810975,0.0002963937,0.1873593,0.00009864552,0.00002743303,0.0000704567,0.0002900288,0.000240054,0.0006426692],"genre_scores_gemma":[0.9061292,0.0001362118,0.09307932,0.00001688659,0.00001278436,0.00005441746,0.0002629807,0.00002651096,0.0002815855],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.001882426,"threshold_uncertainty_score":0.009955287,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.07553756125886044,"score_gpt":0.4349310421706648,"score_spread":0.3593934809118043,"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."}}