{"id":"W3040913759","doi":"10.5121/csit.2020.100902","title":"Classiﬁcation of Fatigue in Consumer-grade EEG Using Entropies as Features","year":2020,"lang":"en","type":"article","venue":"","topic":"EEG and Brain-Computer Interfaces","field":"Neuroscience","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Victoria","funders":"University of Victoria","keywords":"Computer science; Naive Bayes classifier; Support vector machine; Electroencephalography; Decision tree; Artificial intelligence; Pattern recognition (psychology); Entropy (arrow of time); Medical diagnosis; Headset; k-nearest neighbors algorithm; Machine learning; Sample entropy; Speech recognition","routes":{"ca_aff":true,"ca_fund":true,"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":[0.0005154135,0.0004222367,0.0002697316,0.001256315,0.00009505339,0.0004815131,0.0001753056,0.0003210308,0.001168486],"category_scores_gemma":[0.002590063,0.00007304202,0.0002765942,0.0005834117,0.0001424628,0.0005311463,0.0002262151,0.0002114452,0.0004089918],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00013296,"about_ca_system_score_gemma":0.0000973033,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001437256,"about_ca_topic_score_gemma":0.002140781,"domain_scores_codex":[0.9997261,0.00006825422,0.00002443149,0.00004907549,0.00009908048,0.00003306589],"domain_scores_gemma":[0.9990035,0.0005001427,0.0001577786,0.00006298123,0.0002359083,0.00003965356],"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":[0.002404608,0.0005404736,0.2811068,0.0006079723,0.0004362533,0.0007506826,0.0003024969,0.03182396,0.1279893,0.0005791738,0.003318576,0.5501398],"study_design_scores_gemma":[0.00003533677,0.001062715,0.6431192,0.00005979535,0.0001208309,0.001152043,0.0002232992,0.3122871,0.03970774,0.0007862904,0.001380053,0.0000656471],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9626057,0.0006207037,0.03293612,0.0001171748,0.00006120152,0.0000636167,0.0009120187,0.0004135567,0.002269928],"genre_scores_gemma":[0.9926093,0.0001518722,0.005979253,0.00001659319,0.00002921501,0.00001613145,0.0005934263,0.00001642893,0.0005877897],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.001437256,"threshold_uncertainty_score":0.003908992,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.08970254252424668,"score_gpt":0.3259724387118738,"score_spread":0.2362698961876271,"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."}}