{"id":"W4402464118","doi":"10.11159/icbes24.148","title":"Spectral EEG Microstates for Detection of Mental Disorder","year":2024,"lang":"en","type":"article","venue":"Proceedings of the World Congress on Electrical Engineering and Computer Systems and Science","topic":"EEG and Brain-Computer Interfaces","field":"Neuroscience","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Fundação de Amparo à Pesquisa e Inovação do Espírito Santo; Conselho Nacional de Desenvolvimento Científico e Tecnológico","keywords":"Electroencephalography; Computer science; Artificial intelligence; Pattern recognition (psychology); Psychology; Psychiatry","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001935183,0.0001079162,0.0001504701,0.0001973955,0.0001143069,0.0002335344,0.0002461344,0.00001805421,1.389389e-7],"category_scores_gemma":[0.0000336551,0.00007050025,0.00003968603,0.0005991522,0.0001592978,0.0001594627,0.00007978023,0.0001030402,1.087126e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001788536,"about_ca_system_score_gemma":0.00001172547,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000006733469,"about_ca_topic_score_gemma":4.570873e-7,"domain_scores_codex":[0.9991181,0.000003126659,0.0001766665,0.0003106394,0.0001881072,0.0002033112],"domain_scores_gemma":[0.9996569,0.0001520563,0.00005287923,0.000045457,0.0000456459,0.00004711276],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00001850603,0.00002169505,0.0001693312,0.0002948554,0.000007015111,2.59691e-7,0.0001679406,0.0005102709,0.9682386,0.02213714,0.0001221389,0.008312251],"study_design_scores_gemma":[0.00008807689,0.0001642756,0.0003104564,0.0002325704,0.000003856984,0.00002475724,0.000004927564,0.5829693,0.4149725,0.0000448514,0.001116563,0.00006792384],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9959099,0.000574134,0.001477469,0.0001408208,0.001556224,0.0002427323,0.000004945664,0.00005113041,0.00004259564],"genre_scores_gemma":[0.9995141,0.00001990686,0.0001460146,0.00001777396,0.00006560187,0.00001032765,3.303934e-8,0.000006937574,0.0002192975],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.582459,"threshold_uncertainty_score":0.2874916,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.007484922211061423,"score_gpt":0.2222003076006266,"score_spread":0.2147153853895652,"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."}}