{"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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001781039,0.0001503007,0.0001425739,0.0001789367,0.0003019617,0.0001228302,0.0001707043,0.00003638512,0.00003231683],"category_scores_gemma":[0.0002230507,0.0001514829,0.0001118441,0.0002912848,0.00007190321,0.0001854287,0.00006687888,0.00012437,0.000373842],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00003072026,"about_ca_system_score_gemma":0.00000545191,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00003423685,"about_ca_topic_score_gemma":0.000008508619,"domain_scores_codex":[0.9986238,0.000128474,0.0002328468,0.0005032017,0.0001725856,0.000339022],"domain_scores_gemma":[0.9984098,0.001224562,0.0001197943,0.0001453357,0.00004134372,0.00005918473],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.00001701443,0.00004904996,0.001682056,0.00009689407,0.00002309218,0.00003789203,0.001982708,0.00147787,0.7359855,0.000007849475,0.00608182,0.2525583],"study_design_scores_gemma":[0.001513023,0.0001356672,0.01624264,0.0003147142,0.00005360044,0.0000669849,0.0006013787,0.2217336,0.7557976,0.00182553,0.000910823,0.0008044618],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.7038903,0.000002139692,0.2919763,0.002611905,0.0006391371,0.0003544201,0.0001200703,0.0003055728,0.0001001827],"genre_scores_gemma":[0.993853,0.000003979683,0.0006058575,0.004996082,0.0002764587,0.00008124727,0.0001217105,0.00002970727,0.0000319047],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.2913705,"threshold_uncertainty_score":0.6177294,"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."}}