{"id":"W4376869137","doi":"10.1016/j.patrec.2023.05.011","title":"Distilling EEG representations via capsules for affective computing","year":2023,"lang":"en","type":"article","venue":"Pattern Recognition Letters","topic":"EEG and Brain-Computer Interfaces","field":"Neuroscience","cited_by":13,"is_retracted":false,"has_abstract":false,"ca_institutions":"Queen's University","funders":"","keywords":"Computer science; Pipeline (software); Discriminative model; Task (project management); Electroencephalography; Artificial intelligence; Machine learning; Software deployment","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":[0.0004906988,0.0005631732,0.0002969514,0.000404951,0.0002205122,0.001146112,0.0004839259,0.0004868384,0.003471534],"category_scores_gemma":[0.003866826,0.000240227,0.0004199225,0.0008141793,0.0004037907,0.001357585,0.001334651,0.0009558367,0.0008003413],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002187258,"about_ca_system_score_gemma":0.000398587,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0009973276,"about_ca_topic_score_gemma":0.00138698,"domain_scores_codex":[0.9998063,0.00006271525,0.00001280324,0.00004742428,0.0000394928,0.00003128425],"domain_scores_gemma":[0.9995035,0.0002227376,0.00005494619,0.0000864612,0.00007602051,0.00005630528],"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":[0.001022119,0.0001899622,0.002304261,0.0002961092,0.00009531109,0.0002533809,0.0002732726,0.08124284,0.1552874,0.03933124,0.004831506,0.7148725],"study_design_scores_gemma":[0.00003282692,0.0002013779,0.002545783,0.00003211861,0.00002992716,0.0001247225,0.00008740539,0.9292942,0.02761446,0.03604524,0.00395177,0.00004013375],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.04352969,0.0002841359,0.9525907,0.0003543795,0.0001716048,0.00004854168,0.0002234174,0.0005997869,0.002197806],"genre_scores_gemma":[0.7628446,0.0005662967,0.2321851,0.0001674781,0.0001643629,0.000084392,0.0004136056,0.0001954506,0.003378838],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.003471534,"threshold_uncertainty_score":0.01161343,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.06082139151731225,"score_gpt":0.3127922280268336,"score_spread":0.2519708365095213,"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."}}