{"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":"codex-gemma-dda1882f352a","candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0002111119,0.0000882353,0.0001211207,0.0003020164,0.00003778076,0.00006234319,0.000174859,0.00007107256,0.0009136329],"category_scores_gemma":[0.00000691202,0.00008756758,0.00008620983,0.0001010402,0.00002918278,0.0001939566,0.00002580506,0.0001554956,0.0002368875],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00005342976,"about_ca_system_score_gemma":0.00004724451,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000008885357,"about_ca_topic_score_gemma":8.827083e-7,"domain_scores_codex":[0.9990806,0.000067054,0.0003763668,0.0001486884,0.0002356591,0.00009165903],"domain_scores_gemma":[0.9987767,0.00009151823,0.0003542413,0.0001010982,0.0005893654,0.00008703826],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"observational","study_design_scores_codex":[0.0002133854,0.0009459268,0.006894661,0.00003111964,0.0004659224,0.0000190825,0.0009486505,0.0008160749,0.005513555,0.009294525,0.002829722,0.9720274],"study_design_scores_gemma":[0.02875361,0.002390861,0.4119426,0.00173596,0.0007545152,0.007867072,0.002100773,0.2152174,0.00454353,0.06330264,0.2589731,0.00241804],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.3140702,0.00005981772,0.6797282,0.0008671322,0.001688499,0.0002317415,0.00001888183,0.00002556815,0.003310007],"genre_scores_gemma":[0.9706057,0.0000156699,0.02744182,0.0009781017,0.0007540135,0.00001091069,0.00005100725,0.00001483106,0.0001279683],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9696093,"threshold_uncertainty_score":0.9999996,"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."}}