{"id":"W3195760859","doi":"10.23977/acss.2021.050109","title":"Testing the feasibility of EEG signals for emotion recognition","year":2021,"lang":"en","type":"article","venue":"Advances in Computer Signals and Systems","topic":"Emotion and Mood Recognition","field":"Psychology","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Linear discriminant analysis; Principal component analysis; Pattern recognition (psychology); Preprocessor; Computer science; Electroencephalography; Artificial intelligence; Naive Bayes classifier; Speech recognition; Merge (version control); Bayes' theorem; Data set; Data pre-processing; Component analysis; Bayesian probability; Psychology; Support vector machine","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.005698351,0.0009607368,0.0004159983,0.0008224451,0.0003305883,0.001159073,0.0006528486,0.0008879395,0.002418836],"category_scores_gemma":[0.02579976,0.0002030419,0.0006066878,0.0004300134,0.0004598048,0.001493296,0.00104721,0.0005684028,0.00154004],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001345658,"about_ca_system_score_gemma":0.0003640026,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0009649581,"about_ca_topic_score_gemma":0.0005644598,"domain_scores_codex":[0.9968584,0.00115907,0.0002811,0.000710046,0.0006957138,0.0002958039],"domain_scores_gemma":[0.9874702,0.008965792,0.0003758883,0.0008880813,0.002016652,0.0002833622],"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":[0.008296518,0.003009061,0.08903403,0.00112138,0.0008343347,0.000828665,0.0007731358,0.02991096,0.2483026,0.0018013,0.00623532,0.6098527],"study_design_scores_gemma":[0.0005748247,0.00885767,0.274275,0.000148619,0.0004410216,0.001340902,0.001299434,0.5343052,0.1674363,0.002565188,0.008606581,0.0001494337],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9069017,0.0005103099,0.08352886,0.0005867462,0.0003564502,0.0003866541,0.0009936476,0.001148978,0.005586626],"genre_scores_gemma":[0.9610084,0.0001137181,0.03578192,0.0000826498,0.00007214271,0.0001943135,0.001361319,0.00008527819,0.001300174],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.005698351,"threshold_uncertainty_score":0.03013611,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1410715386708055,"score_gpt":0.3676591889162246,"score_spread":0.2265876502454191,"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."}}