Combining and comparing EEG, peripheral physiology and eye-related measures for the assessment of mental workload

Abstract

While studies exist that compare different physiological variables with respect to their association with mental workload, it is still largely unclear which variables supply the best information about momentary workload of an individual and what is the benefit of combining them. We investigated workload using the n-back task, controlling for body movements and visual input. We recorded EEG, skin conductance, respiration, ECG, pupil size and eye blinks of 14 subjects. Various variables were extracted from these recordings and used as features in individually tuned classification models. Online classification was simulated by using the first part of the data as training set and the last part of the data for testing the models. The results indicate that EEG performs best, followed by eye related measures and peripheral physiology. Combining variables from different sensors did not significantly improve workload assessment over the best performing sensor alone. Best classification accuracy, a little over 90%, was reached for distinguishing between high and low workload on the basis of 2 min segments of EEG and eye related variables. A similar and not significantly different performance of 86% was reached using only EEG from single electrode location Pz.

Social Touch in Human–Computer Interaction

“…This pleads for setting a uniform set of validated and standardized measures that covers the different levels and that is robust and sensitive to the hypothesized effects of social touch. This set could include basic physiological measures known to vary with emotional experience [e.g., heart rate variability and skin conductance; Hogervorst et al 2014]; psychological and social measures reflecting trust, proximity, togetherness, and social presence (IJsselsteijn et al 2003;Van Bel et al 2008;van Bel et al 2009), and behavioral measures, e.g., quantifying compliance and performance. Please note though that each set of measures will have its own pitfalls.…”

Probe-Independent EEG Assessment of Mental Workload in Pilots

“…Estimates of the EEG power in discrete frequency ranges that have known clinical significance commonly appear as input features in the brain-machine interface literature, including prior work on mental workload measurement [11][12][13][14][15][16]. As a preliminary benchmark for later comparison with more advanced processing, we evaluated the performance of a classifier that used only EEG spectral band power measurements as input features.…”

EEG-based mental workload recognition related to multitasking

“…Methods to measure mental workload include subjective methods using participant filled forms [6] and objective methods involving the usage of psychophysiological measurements [7]. It is found that electroencephalography (EEG) provides one of the best methods to measure mental workload, with its high temporal resolution [7].…”

“…It is found that electroencephalography (EEG) provides one of the best methods to measure mental workload, with its high temporal resolution [7]. In the present study, mental workload related to multitasking activity is explored using EEG features and an algorithm for the recognition of the different levels of mental workload is proposed.…”