{"id":"W4394575831","doi":"10.1186/s12883-024-03616-0","title":"Improving emotion perception in cochlear implant users: insights from machine learning analysis of EEG signals","year":2024,"lang":"en","type":"article","venue":"BMC Neurology","topic":"Hearing Loss and Rehabilitation","field":"Neuroscience","cited_by":7,"is_retracted":false,"has_abstract":true,"ca_institutions":"International Laboratory for Brain, Music and Sound Research; Centre for Research on Brain Language and Music; Université de Montréal; Trent University; McGill University Health Centre","funders":"Institut de Valorisation des Données","keywords":"Cochlear implant; Perception; Sound perception; Rehabilitation; Electroencephalography; Audiology; Psychology; Speech perception; Auditory perception; Cognitive psychology; Medicine; 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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001947342,0.0001029462,0.000247281,0.0006483495,0.00005449714,0.00003288953,0.00009538951,0.0001112467,0.00008519144],"category_scores_gemma":[0.000500245,0.00008933181,0.0001110233,0.0007992248,0.00006790173,0.0001478994,0.00004805737,0.0003072429,0.00002599088],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00002298287,"about_ca_system_score_gemma":0.00003111192,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001165721,"about_ca_topic_score_gemma":0.0002704203,"domain_scores_codex":[0.9982884,0.0005937124,0.0003243476,0.0004733643,0.0001532201,0.0001669675],"domain_scores_gemma":[0.9987724,0.0009544733,0.00007395097,0.0001446264,0.00001781143,0.00003666697],"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":[0.00006012141,0.00003564661,0.03162359,0.00004118606,0.00001050067,0.00002576671,0.001119626,0.01611037,0.946911,0.0001627859,0.000001834948,0.003897564],"study_design_scores_gemma":[0.0001255868,0.000254784,0.5728821,0.00001245359,0.00006184498,0.000004745746,0.00003383712,0.4234016,0.00288781,0.0002179905,0.00005173478,0.00006546098],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9968498,0.00006410047,0.0024301,0.0001427873,0.0002637612,0.0001229078,0.00001411997,0.00007706378,0.00003539999],"genre_scores_gemma":[0.9995914,0.00002723865,0.0001211216,0.0001638652,0.00003261961,0.000006706333,0.00002162627,0.00001417713,0.00002122738],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.9440232,"threshold_uncertainty_score":0.3642846,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02734463656217105,"score_gpt":0.2788541088159427,"score_spread":0.2515094722537717,"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."}}