{"id":"W4280546261","doi":"10.1109/syscon53536.2022.9773832","title":"A Deep CNN System for Classification of Emotions Using EEG Signals","year":2022,"lang":"en","type":"article","venue":"2022 IEEE International Systems Conference (SysCon)","topic":"EEG and Brain-Computer Interfaces","field":"Neuroscience","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Queen's University","funders":"","keywords":"Electroencephalography; Computer science; Artificial intelligence; Speech recognition; Pattern recognition (psychology); Deep learning; 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":[0.0003200794,0.0009631816,0.0004102249,0.0004323103,0.0003002944,0.0004781122,0.0008552769,0.0006711864,0.003462532],"category_scores_gemma":[0.0004960482,0.0003477404,0.0005372065,0.0003896531,0.0001662923,0.0004555901,0.0005004816,0.0009350615,0.001282599],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006423395,"about_ca_system_score_gemma":0.0006003123,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0143885,"about_ca_topic_score_gemma":0.0158344,"domain_scores_codex":[0.999853,0.00001148115,0.000009285581,0.00005140081,0.00003150058,0.00004331951],"domain_scores_gemma":[0.9999063,0.00001673247,0.00000937142,0.0000122612,0.00004607459,0.000009167025],"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":[0.0007036313,0.0003986162,0.004168967,0.0001864989,0.0002601344,0.0003821648,0.00009132586,0.08422754,0.1020862,0.002067455,0.01708823,0.7883391],"study_design_scores_gemma":[0.00002332828,0.0001515945,0.003483044,0.00002617719,0.00005960854,0.0001072759,0.00001814258,0.9685412,0.02348421,0.0008309077,0.003251543,0.00002294899],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.2110278,0.003474587,0.7546622,0.0008983599,0.001018346,0.0003466181,0.003139981,0.01279554,0.01263655],"genre_scores_gemma":[0.8285028,0.001177249,0.140885,0.0004621819,0.0001348311,0.0003205033,0.004360917,0.000152815,0.02400358],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.0143885,"threshold_uncertainty_score":0.02860951,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1193441714734933,"score_gpt":0.3266080337730554,"score_spread":0.2072638622995621,"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."}}