That study gives an overview of the general magnitude and direction of effects of the different conditions on the studied variables.īesides examining how well different variables can be used to estimate workload on their own, we examine to what extent combination of different variables improves performance. The same data have been analyzed on a group level in Brouwer et al. While analyses are performed offline, we simulate an online 1 situation, where our classification models are trained on data acquired at the start of the experiment and tested on data acquired at the end of the experiment, therewith avoiding inflation of classification accuracy due to time dependencies. Classification analyses are used to get an impression of the quality of workload estimation within an individual. We here aim to provide an overview of the workload assessment performance of a rather broad range of variables within the context of an experiment in which visual input and the amount of body movements are constant across workload levels. Finally, many workload studies suffer from experimental flaws in which workload levels are confounded with for instance body movements (potentially affecting heart rate and related variables) or visual information processing (potentially affecting eye- and EEG based variables). On the other hand, physiological responses to workload may be consistent within and not between individuals, which would result in variables that are seemingly non-responsive to workload at a group level while they are actually valuable for assessing workload on an individual basis. Associations between physiological variables and workload as found using a group level analysis may not generalize to the case of assessing workload in an individual since they may not be sufficiently strong to reliably assess workload at a certain moment in time for a single individual. Secondly, variables are often analyzed and reported at a group level rather than used to assess workload in an individual. Firstly, only a limited set of variables is recorded and analyzed in each study, precluding easy comparison of performance across variables. It is not easy to answer this question based on the current literature because of several complications. (2012).Ī question that arises when one aims to put this knowledge into practical use is which variable(s) one should measure in order to get the best workload assessment for a specific individual. These include heart rate (e.g., studies as reviewed by Vogt et al., 2006), different types of heart rate variability (reviewed by Hancock et al., 1985 Aasman et al., 1987), pupil size (reviewed by Beatty, 1982 May et al., 1990 Porter et al., 2007 Hampson et al., 2010), eye blink frequency and duration ( Wilson and Fisher, 1991 Brookings et al., 1996 Veltman and Gaillard, 1996, 1998), electrodermal measures ( Kohlisch and Schaefer, 1996 Reimer and Mehler, 2011), respiration frequency ( Wientjes, 1992 Mehler et al., 2009 Karavidas et al., 2010) and various variables derived from EEG (most prominently power in the alpha and theta band-reviewed by Brouwer et al. In the literature, mental workload has been associated with a range of physiological variables. A similar and not significantly different performance of 86% was reached using only EEG from single electrode location Pz. 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. Combining variables from different sensors did not significantly improve workload assessment over the best performing sensor alone. The results indicate that EEG performs best, followed by eye related measures and peripheral physiology. 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. Various variables were extracted from these recordings and used as features in individually tuned classification models.
Ekg vs eeg skin#
We recorded EEG, skin conductance, respiration, ECG, pupil size and eye blinks of 14 subjects.
We investigated workload using the n-back task, controlling for body movements and visual input. 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.