{"id":"W4221074462","doi":"10.1016/j.dcn.2022.101096","title":"A practical guide to applying machine learning to infant EEG data","year":2022,"lang":"en","type":"article","venue":"Developmental Cognitive Neuroscience","topic":"Neural dynamics and brain function","field":"Neuroscience","cited_by":16,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of British Columbia; Pacific Centre for Reproductive Medicine","funders":"Natural Sciences and Engineering Research Council of Canada; Canadian Institute for Advanced Research","keywords":"Electroencephalography; Oddball paradigm; Artificial intelligence; Computer science; Cognition; Psychology; Machine learning; Perception; Pattern recognition (psychology); Cognitive psychology; Speech recognition; Event-related potential; 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":[0.001490981,0.001233621,0.0006399509,0.001606408,0.0004221323,0.001768947,0.002004241,0.001452092,0.1017464],"category_scores_gemma":[0.01140524,0.0009883665,0.0008673799,0.001759528,0.0003779669,0.001262143,0.001184713,0.002869814,0.07457876],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003785697,"about_ca_system_score_gemma":0.001132022,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002126666,"about_ca_topic_score_gemma":0.004644501,"domain_scores_codex":[0.9991795,0.0002071879,0.0001239645,0.0001496979,0.0003023805,0.00003718586],"domain_scores_gemma":[0.9967511,0.001789953,0.0001324936,0.0004133953,0.0008074471,0.0001057774],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","study_design_scores_codex":[0.00006812183,0.00006851929,0.0005246654,0.001309028,0.0000628414,0.0003159468,0.0001287031,0.006767021,0.007238909,0.02027545,0.548133,0.4151079],"study_design_scores_gemma":[0.00006772961,0.00008652527,0.001433541,0.0005377997,0.00001896229,0.0009073539,0.00006382395,0.0461637,0.005411222,0.05595234,0.8892623,0.00009471221],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.0006866337,0.002425405,0.9339569,0.001134911,0.0006746257,0.0005051561,0.0158046,0.03180276,0.0130089],"genre_scores_gemma":[0.004406177,0.002510376,0.9527874,0.00116691,0.0003619683,0.001381199,0.0111819,0.00563174,0.02057221],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.1017464,"threshold_uncertainty_score":0.3403757,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.09471033399982041,"score_gpt":0.3502968455393057,"score_spread":0.2555865115394853,"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."}}