{"id":"W2945457185","doi":"10.1109/ner.2019.8716977","title":"Performance Comparison of Automated EEG Enhancement Algorithms for Mental Workload Assessment of Ambulant Users","year":2019,"lang":"en","type":"article","venue":"","topic":"Human-Automation Interaction and Safety","field":"Psychology","cited_by":12,"is_retracted":false,"has_abstract":true,"ca_institutions":"Université Laval; Thales (Canada); Institut National de la Recherche Scientifique; Université du Québec à Montréal","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Workload; Wearable computer; Computer science; Robustness (evolution); Electroencephalography; Brain–computer interface; Artifact (error); Feature extraction; Human–computer interaction; Artificial intelligence; Simulation; Real-time computing; Embedded system; Psychology","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":[0.001113444,0.001196507,0.000622181,0.001366217,0.0002492052,0.0007822511,0.0005170982,0.0008093971,0.00108432],"category_scores_gemma":[0.005974774,0.0001578367,0.0005944467,0.0004666772,0.0002196638,0.000550435,0.0005256645,0.0004284839,0.0003993437],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002277076,"about_ca_system_score_gemma":0.000507166,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002516845,"about_ca_topic_score_gemma":0.002307084,"domain_scores_codex":[0.9993522,0.0001492721,0.00008004977,0.0001809061,0.0001590326,0.00007857989],"domain_scores_gemma":[0.9980044,0.00116582,0.0001564888,0.0001055132,0.0004907018,0.00007705583],"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":[0.005839676,0.001100546,0.01958659,0.0006612478,0.0005628269,0.0002159952,0.0002847962,0.05281284,0.05596355,0.0005568974,0.002979962,0.8594351],"study_design_scores_gemma":[0.0002977708,0.002855534,0.1289347,0.0001020623,0.0004188474,0.0008929379,0.0003001686,0.8169463,0.04605425,0.0006534462,0.002418989,0.0001249392],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8591184,0.002874096,0.1328095,0.0001836069,0.0002464583,0.0002029252,0.000463544,0.001691182,0.002410318],"genre_scores_gemma":[0.9280897,0.001011177,0.06787753,0.0001281767,0.0001122567,0.0001829771,0.001085524,0.0001025629,0.00140998],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.002516845,"threshold_uncertainty_score":0.005888522,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0452199527436182,"score_gpt":0.4401926050407218,"score_spread":0.3949726522971037,"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."}}