{"id":"W2774910776","doi":"10.1109/smc.2017.8122573","title":"EEG coupling features: Towards mental workload measurement based on wearables","year":2017,"lang":"en","type":"article","venue":"","topic":"Human-Automation Interaction and Safety","field":"Psychology","cited_by":8,"is_retracted":false,"has_abstract":true,"ca_institutions":"Thales (Canada); Institut National de la Recherche Scientifique; Université du Québec à Montréal","funders":"","keywords":"Workload; Computer science; Wearable computer; Software portability; Electroencephalography; Task (project management); Real-time computing; Wearable technology; Human–computer interaction; SIGNAL (programming language); Simulation; Artificial intelligence; Engineering; Embedded system","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":["insufficient_payload"],"consensus_categories":["insufficient_payload"],"category_scores_codex":[0.0004551635,0.0001435458,0.0001412101,0.00008278213,0.0006017944,0.0001911985,0.0002718887,0.00009369543,0.02457418],"category_scores_gemma":[0.00008285801,0.0001192403,0.00009978149,0.00002495553,0.00004772982,0.0001037448,0.0000245314,0.0001933101,0.002033439],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001405733,"about_ca_system_score_gemma":0.00003965385,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0002716858,"about_ca_topic_score_gemma":0.0001771509,"domain_scores_codex":[0.9988255,0.00003806957,0.0002104561,0.0002721964,0.0004368276,0.0002169384],"domain_scores_gemma":[0.998993,0.00002961566,0.0001384496,0.0006472637,0.0001026652,0.00008902144],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"observational","study_design_scores_codex":[0.001873508,0.001992568,0.006292786,0.00005100937,0.0005686264,0.00006310422,0.005732901,0.001252142,0.00363799,0.03534051,0.7666331,0.1765618],"study_design_scores_gemma":[0.005634758,0.0003751685,0.6333306,0.0005037107,0.00004729456,0.00001533048,0.003146288,0.01675433,0.003864011,0.0002158445,0.3353362,0.0007764758],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"other","genre_gemma":"empirical","genre_scores_codex":[0.02043297,0.00007414952,0.001589012,0.006392663,0.003602036,0.0002700296,0.000008232651,0.0002341807,0.9673967],"genre_scores_gemma":[0.9736369,0.000005436135,0.0002161915,0.001512731,0.0001204444,0.0000374843,0.000004988969,0.00001542155,0.02445044],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9532039,"threshold_uncertainty_score":0.9987436,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.07373677815982814,"score_gpt":0.3834375522873248,"score_spread":0.3097007741274966,"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."}}