{"id":"W2973367259","doi":"10.5120/ijca2019919352","title":"EEG based Emotion Recognition using SVM and LibSVM","year":2019,"lang":"en","type":"article","venue":"International Journal of Computer Applications","topic":"Emotion and Mood Recognition","field":"Psychology","cited_by":8,"is_retracted":false,"has_abstract":true,"ca_institutions":"Lakehead University","funders":"","keywords":"Computer science; Support vector machine; Electroencephalography; Pattern recognition (psychology); Artificial intelligence; Emotion recognition; Emotion detection; Speech recognition; Machine 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":[0.0006261142,0.001172107,0.000733919,0.001814606,0.0003186815,0.001061938,0.0009340357,0.0006360302,0.01141125],"category_scores_gemma":[0.001987305,0.0002849368,0.000834066,0.001402887,0.0001205455,0.0009687241,0.0007979985,0.001162183,0.006306626],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002946699,"about_ca_system_score_gemma":0.000472437,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001254213,"about_ca_topic_score_gemma":0.00143485,"domain_scores_codex":[0.9995266,0.00005653451,0.00006313795,0.0001362116,0.0001523548,0.0000650553],"domain_scores_gemma":[0.999552,0.00009105034,0.00004364687,0.0000890711,0.000191944,0.00003238559],"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":[0.0005262907,0.0003560019,0.004631847,0.0005423203,0.000250686,0.0002848227,0.00009775973,0.008994655,0.04106009,0.001590616,0.05332048,0.8883445],"study_design_scores_gemma":[0.0001599386,0.0005145916,0.03011293,0.0002142841,0.0001751301,0.001346921,0.0002347472,0.7889278,0.1132975,0.007885372,0.05695192,0.0001788438],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.0586005,0.002284933,0.8385049,0.000536054,0.001080408,0.000646472,0.01136422,0.07913039,0.007852189],"genre_scores_gemma":[0.3102989,0.001694258,0.6406304,0.0004647715,0.0003874447,0.001813876,0.02647953,0.003032143,0.01519874],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.01141125,"threshold_uncertainty_score":0.03817445,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03704013402595299,"score_gpt":0.335241564104393,"score_spread":0.2982014300784401,"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."}}