{"id":"W4220842428","doi":"10.3390/s22062346","title":"Automated Feature Extraction on AsMap for Emotion Classification Using EEG","year":2022,"lang":"en","type":"article","venue":"Sensors","topic":"EEG and Brain-Computer Interfaces","field":"Neuroscience","cited_by":71,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Calgary","funders":"","keywords":"Feature extraction; Artificial intelligence; Pattern recognition (psychology); Computer science; Electroencephalography; Support vector machine; Convolutional neural network; Differential entropy; Entropy (arrow of time); Feature (linguistics); Speech recognition; Principle of maximum entropy; Rényi entropy; Psychology","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":[0.0001350068,0.00009306334,0.00007858502,0.0001097441,0.0004255344,0.00005291491,0.0001114809,0.00004344342,0.00002856849],"category_scores_gemma":[0.00009909312,0.00009157506,0.00005340475,0.0002067587,0.00002085394,0.00008994078,0.00002522315,0.0001769963,0.0000154109],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001288707,"about_ca_system_score_gemma":0.00001668529,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000004812106,"about_ca_topic_score_gemma":7.595207e-7,"domain_scores_codex":[0.9990371,0.0001533395,0.0001148961,0.0003228491,0.0002098472,0.0001619119],"domain_scores_gemma":[0.9995124,0.0001653995,0.0001139419,0.0001555209,0.00002388259,0.00002881553],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00006702398,0.00007625009,0.00002218623,0.00001103745,0.00000259749,0.000003895286,0.0003039493,0.02797944,0.9642051,0.0006597386,0.00408727,0.00258154],"study_design_scores_gemma":[0.0002171767,0.0001600022,0.001620274,0.000009771422,0.000006545959,0.00005083633,0.0002698675,0.7678478,0.2158731,0.0001099104,0.01372457,0.0001101712],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9957626,0.00000322765,0.0005484122,0.001255433,0.001127226,0.0002982512,0.00003069883,0.0004250593,0.0005490221],"genre_scores_gemma":[0.9974044,0.000001118424,0.0007799086,0.0004167773,0.00008568901,0.00001513472,0.00001342902,0.00001711825,0.001266412],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.748332,"threshold_uncertainty_score":0.3734323,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.07217421529605517,"score_gpt":0.3344619873014697,"score_spread":0.2622877720054145,"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."}}