دورية أكاديمية
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Evaluating the performance of the cognitive workload model with subjective endorsement in addition to EEG.

التفاصيل البيبلوغرافية
العنوان: Evaluating the performance of the cognitive workload model with subjective endorsement in addition to EEG.
المؤلفون: Gogna Y; ICE Department, Dr. B. R. Ambedkar NIT Jalandhar, GT Road Bye-Pass, Jalandhar, Punjab, 144008, India. gognayaminiice@gmail.com., Tiwari S; ICE Department, Dr. B. R. Ambedkar NIT Jalandhar, GT Road Bye-Pass, Jalandhar, Punjab, 144008, India., Singla R; ICE Department, Dr. B. R. Ambedkar NIT Jalandhar, GT Road Bye-Pass, Jalandhar, Punjab, 144008, India.
المصدر: Medical & biological engineering & computing [Med Biol Eng Comput] 2024 Jul; Vol. 62 (7), pp. 2019-2036. Date of Electronic Publication: 2024 Mar 03.
نوع المنشور: Journal Article
اللغة: English
بيانات الدورية: Publisher: Springer Country of Publication: United States NLM ID: 7704869 Publication Model: Print-Electronic Cited Medium: Internet ISSN: 1741-0444 (Electronic) Linking ISSN: 01400118 NLM ISO Abbreviation: Med Biol Eng Comput Subsets: MEDLINE
أسماء مطبوعة: Publication: New York, NY : Springer
Original Publication: Stevenage, Eng., Peregrinus.
مواضيع طبية MeSH: Electroencephalography*/methods , Workload* , Cognition*/physiology , Support Vector Machine*, Humans ; Male ; Female ; Adult ; Young Adult ; Task Performance and Analysis
مستخلص: The aptitude-oriented exercises from almost all domains impose cognitive load on their operators. Evaluating such load poses several challenges owing to many factors like measurement mode and complexity, nature of the load, overloading conditions, etc. Nevertheless, the physiological measurement of a specific genre of cognitive load and subjective measurement have not been reported along with each other. In this study, the electroencephalography (EEG)-driven machine learning (Support Vector Machine (SVM)) model is sought along with the support of NASA's Task Load Index (NASA-TLX) rating scale for a novel purpose in workload exploration of operators. The Cognitive Load Theory (CLT) was used as the foundation to design the intrinsic stimulus (Spot the Difference task), as most workloads operators are exposed to are notably intrinsic. The SVM-based three-level classification accuracy ranged from 85.4 to 97.4% (p < 0.05), and the NASA-TLX-based three-level classification accuracy ranged from 88.33 to 97.33%. The t-test results show that the neurometric indices contributing to the classification significantly differed (p < 0.05) for every level. The NASA-TLX scale was utilised for validation in its basic form after the validity (Pearson correlation coefficients 0.338 to 0.805 (p < 0.05)) and reliability (Cronbach's α = 0.753) test. This modeling is beneficial to phase out particular-level cognitive exercises from the curriculum during under or overload workload (critical) conditions.
(© 2024. International Federation for Medical and Biological Engineering.)
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فهرسة مساهمة: Keywords: Electroencephalography (EEG); Mental workload (MWL); NASA’s Task Load Index (NASA-TLX); Support vector machine (SVM)
تواريخ الأحداث: Date Created: 20240303 Date Completed: 20240620 Latest Revision: 20240620
رمز التحديث: 20240620
DOI: 10.1007/s11517-024-03049-4
PMID: 38433179
قاعدة البيانات: MEDLINE
الوصف
تدمد:1741-0444
DOI:10.1007/s11517-024-03049-4