Reliability of Mental Workload Index Assessed by EEG with Different Electrode Configurations and Signal Pre-Processing Pipelines

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

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.

Reproducible machine learning research in mental workload classification using EEG

This study addresses concerns about reproducibility in scientific research, focusing on the use of electroencephalography (EEG) and machine learning to estimate mental workload. We established guidelines for reproducible machine learning research using EEG and used these to assess the current state of reproducibility in mental workload modeling. We first started by summarizing the current state of reproducibility efforts in machine learning and in EEG. Next, we performed a systematic literature review on Scopus, Web of Science, ACM Digital Library, and Pubmed databases to find studies about reproducibility in mental workload prediction using EEG. All of this previous work was used to formulate guidelines, which we structured along the widely recognized Cross-Industry Standard Process for Data Mining (CRISP-DM) framework. By using these guidelines, researchers can ensure transparency and comprehensiveness of their methodologies, therewith enhancing collaboration and knowledge-sharing within the scientific community, and enhancing the reliability, usability and significance of EEG and machine learning techniques in general. A second systematic literature review extracted machine learning studies that used EEG to estimate mental workload. We evaluated the reproducibility status of these studies using our guidelines. We highlight areas studied and overlooked and identify current challenges for reproducibility. Our main findings include limitations on reporting performance on unseen test data, open sharing of data and code, and reporting of resources essential for training and inference processes.

Biomedical Sensors for Functional Mapping: Techniques, Methods, Experimental and Medical Applications

The rapid advancement of biomedical sensor technology has revolutionized the field of functional mapping in medicine, offering novel and powerful tools for diagnosis, clinical assessment, and rehabilitation [...].