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

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-bas ed 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%. Thet-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’sα = 0.753) test. This modeling is beneficial to phase out particular-level cognitive exercises from the curriculum during under or overload workload (critical) conditions.Graphical abstract
Source: Medical and Biological Engineering and Computing - Category: Biomedical Engineering Source Type: research