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

Hogervorst MA, Brouwer AM, van Erp JB - Front Neurosci (2014)

Bottom Line: 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.A similar and not significantly different performance of 86% was reached using only EEG from single electrode location Pz.

View Article: PubMed Central - PubMed

Affiliation: TNO Human Factors, Netherlands Organisation for Applied Scientific Research Soesterberg, Netherlands.

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.

No MeSH data available.


Classification performance in the default condition (2- vs. 0-back, 120 s) for the different sensor types and using all available input. The striped bars show the effect of adding the time feature. Conventions as in Figure 1.
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Figure 4: Classification performance in the default condition (2- vs. 0-back, 120 s) for the different sensor types and using all available input. The striped bars show the effect of adding the time feature. Conventions as in Figure 1.

Mentions: Figure 4 shows the effect of adding the feature time (i.e., the time of measurement, since the start of the experiment) to the model input for the default case (2- vs. 1-back, 120 s of data). Adding time leads to an increase in performance of 9% (significant at p < 0.05) and 5% (not significant) for respectively physiological and eye sensor group models (SVM) when classifying 2- vs. 0-back using 120 s of data. For EEG and “All” the inclusion of time information does not improve performance. Further analysis of the data shows that when classification is more difficult due to shorter time intervals or smaller workload differences (see Figures 3B–D), the potentially beneficial effect of including time decreases.


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

Hogervorst MA, Brouwer AM, van Erp JB - Front Neurosci (2014)

Classification performance in the default condition (2- vs. 0-back, 120 s) for the different sensor types and using all available input. The striped bars show the effect of adding the time feature. Conventions as in Figure 1.
© Copyright Policy - open-access
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC4196537&req=5

Figure 4: Classification performance in the default condition (2- vs. 0-back, 120 s) for the different sensor types and using all available input. The striped bars show the effect of adding the time feature. Conventions as in Figure 1.
Mentions: Figure 4 shows the effect of adding the feature time (i.e., the time of measurement, since the start of the experiment) to the model input for the default case (2- vs. 1-back, 120 s of data). Adding time leads to an increase in performance of 9% (significant at p < 0.05) and 5% (not significant) for respectively physiological and eye sensor group models (SVM) when classifying 2- vs. 0-back using 120 s of data. For EEG and “All” the inclusion of time information does not improve performance. Further analysis of the data shows that when classification is more difficult due to shorter time intervals or smaller workload differences (see Figures 3B–D), the potentially beneficial effect of including time decreases.

Bottom Line: 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.A similar and not significantly different performance of 86% was reached using only EEG from single electrode location Pz.

View Article: PubMed Central - PubMed

Affiliation: TNO Human Factors, Netherlands Organisation for Applied Scientific Research Soesterberg, Netherlands.

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.

No MeSH data available.