STEW: Simultaneous Task EEG Workload Data Set

• Published in: IEEE transactions on neural systems and rehabilitation engineering Vol. 26; no. 11; pp. 2106 - 2114
• Main Authors: Lim, W. L., Sourina, O., Wang, L. P.
• Format: Journal Article
• Language: English
• Published: United States IEEE 01.11.2018; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Adult; Algorithms; Brain-Computer Interfaces; Classification; Cognitive ability; Data bases; Databases, Factual; EEG; Electroencephalography; Electroencephalography (EEG); Electroencephalography - statistics & numerical data; Electronic mail; Frequency measurement; Humans; Male; mental workload; Models, Neurological; Multitasking; Open Access; open access dataset; Operator performance; Regression analysis; Regression models; Reproducibility of Results; Support Vector Machine; Support vector machines; Task analysis; Working conditions; Workload; Workload - psychology; Workloads
• DOI: 10.1109/TNSRE.2018.2872924
• ISSN: 1534-4320; 1558-0210

This paper describes an open access electroencephalography (EEG) data set for multitasking mental workload activity induced by a single-session simultaneous capacity (SIMKAP) experiment with 48 subjects. To validate the database, EEG spectral activity was evaluated with EEGLAB and the significant channels and activities for the experiment are highlighted. Classification performance was evaluated by training a support vector regression model on selected features from neighborhood component analysis based on a nine-point workload rating scale. With a reduced feature dimension, 69% classification accuracy was obtained for 3 identified workload levels from the rating scale with Cohen's kappa of 0.46. Accurate discrimination of mental workload is a desirable outcome in the field of operator performance analysis and BCI development; thus, we hope that our provided database and analyses can contribute to future investigations in this research field.