{"id":"W4387951221","doi":"10.1109/ccece58730.2023.10289007","title":"Multimodal Deep Learning Model for Subject-Independent EEG-based Emotion Recognition","year":2023,"lang":"en","type":"article","venue":"","topic":"Emotion and Mood Recognition","field":"Psychology","cited_by":11,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Winnipeg","funders":"HORIZON EUROPE Health; University of Winnipeg","keywords":"Electroencephalography; Emotion recognition; Computer science; Artificial intelligence; Deep learning; Eye movement; Emotion classification; Pattern recognition (psychology); Speech recognition; Cognitive psychology; Psychology; 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":[0.0004964132,0.0008011135,0.0004621562,0.0002889019,0.0001334756,0.0003664482,0.0007867279,0.0005619228,0.002365517],"category_scores_gemma":[0.001042296,0.0001845551,0.0006277008,0.0002758027,0.0001837527,0.0004550579,0.0006343772,0.001120197,0.0005996244],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004845012,"about_ca_system_score_gemma":0.0004026082,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003989018,"about_ca_topic_score_gemma":0.005554171,"domain_scores_codex":[0.9998566,0.0000291946,0.000007087593,0.00004926079,0.00002593224,0.00003196148],"domain_scores_gemma":[0.9998472,0.00004850605,0.00001725074,0.00001422359,0.00006066548,0.00001217797],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0006747805,0.0004507046,0.005308142,0.0001792397,0.000307992,0.0002212672,0.0001440598,0.388438,0.05098922,0.003590676,0.00989217,0.5398037],"study_design_scores_gemma":[0.000006400604,0.00004090599,0.0008919144,0.000006774823,0.00001970696,0.00002030094,0.00000728739,0.9952713,0.00219403,0.001035982,0.000499587,0.000005757273],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1071926,0.001554458,0.8838906,0.0006905532,0.0001894955,0.00009556059,0.0007853291,0.001970675,0.003630795],"genre_scores_gemma":[0.9109578,0.0005316636,0.07798209,0.000427201,0.00009688201,0.0002320055,0.001114666,0.00009287058,0.008564886],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.003989018,"threshold_uncertainty_score":0.00793159,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.06495912080176887,"score_gpt":0.3282589480401574,"score_spread":0.2632998272383886,"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."}}