{"id":"W4310191140","doi":"10.3390/s22239282","title":"Feature Selection for Continuous within- and Cross-User EEG-Based Emotion Recognition","year":2022,"lang":"en","type":"article","venue":"Sensors","topic":"EEG and Brain-Computer Interfaces","field":"Neuroscience","cited_by":4,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of New Brunswick","funders":"","keywords":"Modalities; Electroencephalography; Feature selection; Computer science; Emotion recognition; Feature (linguistics); Selection (genetic algorithm); Affective computing; Emotion classification; Artificial intelligence; Feature engineering; Machine learning; Human–computer interaction; Psychology; Deep learning","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":[0.00180501,0.001052687,0.0007058417,0.000586495,0.0002924237,0.0005368801,0.0006733137,0.0005081763,0.00148094],"category_scores_gemma":[0.004793322,0.000154019,0.0008291882,0.0004840239,0.0002314886,0.0005384766,0.0007034661,0.0006950874,0.0008185593],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002755096,"about_ca_system_score_gemma":0.0003160865,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002228031,"about_ca_topic_score_gemma":0.002510289,"domain_scores_codex":[0.9990534,0.0003171674,0.00006505912,0.000240142,0.0001933757,0.0001308285],"domain_scores_gemma":[0.9988868,0.0005246305,0.00006973192,0.0001862077,0.0002860765,0.00004666469],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.002625249,0.001262821,0.01759865,0.0002651902,0.0003771826,0.000398589,0.000241584,0.04376167,0.0836618,0.000715733,0.01348507,0.8356065],"study_design_scores_gemma":[0.0001441407,0.001048724,0.05793915,0.0000385409,0.0001675763,0.0005989172,0.0001832812,0.8768729,0.05712301,0.001943224,0.003863655,0.00007686638],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.4721499,0.001382526,0.5168135,0.0002733627,0.0002715296,0.0003080234,0.001974346,0.005366165,0.001460736],"genre_scores_gemma":[0.9047316,0.0002269234,0.08860486,0.0001030622,0.00005755925,0.0003852005,0.004472601,0.0001886619,0.001229571],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.002228031,"threshold_uncertainty_score":0.009545922,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02331969528218748,"score_gpt":0.2726814638179166,"score_spread":0.2493617685357291,"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."}}