{"id":"W4372259783","doi":"10.1109/icassp49357.2023.10094939","title":"EEG Emotion Recognition Via Ensemble Learning Representations","year":2023,"lang":"en","type":"article","venue":"","topic":"EEG and Brain-Computer Interfaces","field":"Neuroscience","cited_by":5,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"","keywords":"Electroencephalography; Discriminative model; Computer science; Artificial intelligence; Emotion recognition; Arousal; Focus (optics); Pattern recognition (psychology); Valence (chemistry); Speech recognition; Feature extraction; Emotion classification; Feature (linguistics); Affective computing; Psychology; Neuroscience","routes":{"ca_aff":true,"ca_fund":false,"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":[0.0004700607,0.0008031991,0.0005730262,0.0005234156,0.0001242303,0.0005123209,0.0004772113,0.0003769055,0.0009763649],"category_scores_gemma":[0.001485369,0.0001507138,0.0006071812,0.0005377285,0.000145317,0.0007649084,0.0006717488,0.0008745255,0.0005042526],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001827225,"about_ca_system_score_gemma":0.0001650255,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0009672025,"about_ca_topic_score_gemma":0.001088949,"domain_scores_codex":[0.9997165,0.00005931047,0.00001476405,0.0001009746,0.00006453957,0.00004382307],"domain_scores_gemma":[0.9996709,0.00008649861,0.00004098471,0.00005129914,0.0001320172,0.00001825644],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"not_applicable","study_design_scores_codex":[0.0003136694,0.0002055763,0.006023737,0.00006736007,0.000227991,0.0001306852,0.00009563394,0.08812599,0.03611685,0.002029042,0.006243028,0.8604205],"study_design_scores_gemma":[0.000006935725,0.00007864166,0.003992137,0.000009436095,0.00005213247,0.00008172567,0.00002910405,0.9860152,0.005916587,0.002878695,0.0009259985,0.00001348555],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1511493,0.001458032,0.841826,0.0003909211,0.0002332677,0.00006148459,0.0004669094,0.001488492,0.002925508],"genre_scores_gemma":[0.9273237,0.0008896929,0.06739095,0.0001461734,0.0001532086,0.00006942882,0.001224764,0.00007054731,0.002731461],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.0009763649,"threshold_uncertainty_score":0.003266215,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.071114467302915,"score_gpt":0.3163450200559189,"score_spread":0.2452305527530039,"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."}}