{"id":"W4409494582","doi":"10.1109/taffc.2025.3562027","title":"Partial Label Learning for Emotion Recognition From EEG","year":2025,"lang":"en","type":"article","venue":"IEEE Transactions on Affective Computing","topic":"EEG and Brain-Computer Interfaces","field":"Neuroscience","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"Queen's University","funders":"","keywords":"Electroencephalography; Emotion recognition; Psychology; Cognitive psychology; Emotion classification; Computer science; Artificial intelligence; Speech recognition; 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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001945314,0.0001787519,0.0001888187,0.0001873919,0.0006477781,0.0001091316,0.0001365261,0.00009000317,0.00002323374],"category_scores_gemma":[0.0001047906,0.0001861181,0.0001234423,0.0003233854,0.00005533018,0.0001567192,0.00000329844,0.0003806841,0.000046867],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0000962519,"about_ca_system_score_gemma":0.00002729762,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00003238225,"about_ca_topic_score_gemma":0.00001022239,"domain_scores_codex":[0.9985369,0.0002972663,0.0002167057,0.0005529569,0.0001284069,0.000267758],"domain_scores_gemma":[0.9975582,0.002112733,0.00009158267,0.0001224509,0.00007206291,0.00004298248],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.0001754002,0.0003056118,0.00003353798,0.00003487917,0.00004159308,0.000002331984,0.0007279263,0.04048988,0.2952053,0.00006566625,0.0001306381,0.6627872],"study_design_scores_gemma":[0.0009065645,0.0002473207,0.0002465757,0.0002087203,0.00003237862,0.000002025657,0.00009455209,0.3071825,0.6899744,0.0007098592,0.0002405814,0.0001545116],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.4358659,0.000005711421,0.5619078,0.0001115709,0.001259038,0.000283325,0.00001744963,0.0001947264,0.0003545513],"genre_scores_gemma":[0.9973712,0.000004499754,0.001822064,0.0004251425,0.0001109089,0.00003224377,0.000004826368,0.00001821435,0.0002109209],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.6626327,"threshold_uncertainty_score":0.7589675,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04141856165419956,"score_gpt":0.3047044322839445,"score_spread":0.263285870629745,"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."}}