Using Wireless Dry EEG System to Detect Mental Workload during Mental Arithmetic

Date

2016-05

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Abstract

Mental workload, the amount of cognitive resources invested during a task, is a crucial metric for performance. Determining the performance of an individual aids in the screening of candidates for, or detection of alertness during, a high-risk task. Three measures for assessing mental workload exist: subjective, performance, and physiological. Of the three, a physiological measure is the most direct type, which can be used to track mental workload as a subject performs tasks. One type of physiological measure is monitoring EEG signal variations. This study examined the efficacy of detecting mental workload in subjects performing mental arithmetic of increasing complexity using a wireless dry EEG system. The level of difficulty was confirmed by performance measures of accuracy in their responses, and the length of response times. The level of difficulty was correlated in most of the subjects to visually distinguishable patterns in time-frequency spectrograms between tasks and breaks within the alpha band (8-12 Hz). Subjects that exhibited more pronounced reduction in alpha band power as the level of difficulty increased achieved the highest response accuracy. Localization of mental activity was accomplished by discretely assessing alpha and theta band power in four brain lobes. Classification of low, medium, and high difficulty levels, as well as rest segments of the recorded EEG, was accomplished using a K-nearest neighbor classifier at an average accuracy of 91%. These findings validate the use of dry EEG as a valid technology that is capable of generating effective physiological measures for detecting mental workload levels.

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Keywords

EEG, Mental workload, Electroencephalography (EEG), Biomedical signal processing, Classification

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