{"id":"W3091525468","doi":"10.1088/1741-2552/abbc27","title":"EEG-based detection of mental workload level and stress: the effect of variation in each state on classification of the other","year":2020,"lang":"en","type":"article","venue":"Journal of Neural Engineering","topic":"EEG and Brain-Computer Interfaces","field":"Neuroscience","cited_by":41,"is_retracted":false,"has_abstract":true,"ca_institutions":"Memorial University of Newfoundland","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Workload; Computer science; Electroencephalography; Brain–computer interface; Classifier (UML); Task (project management); Cognition; Linear discriminant analysis; Artificial intelligence; Human–computer interaction; Psychology; Neuroscience; Engineering","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001560074,0.00005591273,0.0001083778,0.00005137504,0.00001244027,0.000008181457,0.0001151286,0.0000160255,7.581074e-7],"category_scores_gemma":[0.0001549312,0.00003101699,0.00004141425,0.0001484643,0.00001905286,0.00007245942,0.00001348678,0.0001469666,5.046229e-8],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001390547,"about_ca_system_score_gemma":0.000005464576,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000006229638,"about_ca_topic_score_gemma":0.000001817837,"domain_scores_codex":[0.9994013,0.00008858581,0.0002397236,0.00005785661,0.000162845,0.00004974024],"domain_scores_gemma":[0.9993964,0.0002730698,0.0002498346,0.00005110938,0.00001502645,0.00001455744],"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":[0.00008991505,0.000009768591,0.0009092676,0.0000386456,0.000003349582,3.612565e-7,0.0004293497,0.126238,0.8696505,0.000004199922,8.536302e-7,0.002625736],"study_design_scores_gemma":[0.0002788724,0.000313516,0.07054917,0.0001108466,0.000004255632,0.000002596972,0.00001063977,0.2466418,0.682065,0.000001280294,0.000003785326,0.0000182057],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9960366,0.00002786885,0.003264714,0.000436419,0.000135408,0.00008567949,0.00000698293,0.000002790013,0.000003505535],"genre_scores_gemma":[0.9999129,0.000002860511,0.00002624605,0.00003248921,0.00001871085,6.613864e-7,3.21886e-8,0.000004848799,0.000001216325],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.1875856,"threshold_uncertainty_score":0.1264836,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0299324216741683,"score_gpt":0.2458544907233402,"score_spread":0.2159220690491719,"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."}}