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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 for separate and combined sensor groups for SVM, elastic net and a model that combines the outputs from different single feature models (“decision level”). (A) performance in the default condition (2- vs. 0-back, 120 s of data, (B) for comparing 2- vs. 0-back over 30 s of data, (C) for comparing 2- vs. 1-back (120 s of data), (D) for comparing 1- vs. 0-back (120 s of data).
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Figure 3: Classification performance for separate and combined sensor groups for SVM, elastic net and a model that combines the outputs from different single feature models (“decision level”). (A) performance in the default condition (2- vs. 0-back, 120 s of data, (B) for comparing 2- vs. 0-back over 30 s of data, (C) for comparing 2- vs. 1-back (120 s of data), (D) for comparing 1- vs. 0-back (120 s of data).

Mentions: Figure 3 shows the results for different (combinations of) sensor groups of SVM and elastic net, as well as the outcome of combining the outputs of different elastic net models (“decision level”). Shown are the results for the default case of classifying 2 vs. 0-back over 2 min. data segments (a) as well as for more difficult cases: using 30 s data segments (b), or classifying 2 vs. 1-back (c), or 1 vs. 0-back (d). Comparing performance in the default case (Figure 3A) with that of separate sensors (Figure 2) shows that for Physiology, combining the three sensors leads to a (non-significant) increase in performance (accuracy of 0.75 for SVM, compared to 0.70 for the best performing single physiological sensor respiration). Models that include EEG perform significantly better than physiology and eye models (SVM, pairwise comparisons, p < 0.05). Adding sensor groups to the already well performing EEG improves classification accuracy by 3–5% (for adding Physiology or Eye variables with elastic net). For SVM, the combination of physiology and eye measures tends to improve performance relative to either one alone by 7% as well. However, all of these improvements do not reach statistical significance. Using the assumption of a binomial distribution, significance (p < 0.05) is reached for differences of around 10%.


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 for separate and combined sensor groups for SVM, elastic net and a model that combines the outputs from different single feature models (“decision level”). (A) performance in the default condition (2- vs. 0-back, 120 s of data, (B) for comparing 2- vs. 0-back over 30 s of data, (C) for comparing 2- vs. 1-back (120 s of data), (D) for comparing 1- vs. 0-back (120 s of data).
© Copyright Policy - open-access
Related In: Results  -  Collection

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

Figure 3: Classification performance for separate and combined sensor groups for SVM, elastic net and a model that combines the outputs from different single feature models (“decision level”). (A) performance in the default condition (2- vs. 0-back, 120 s of data, (B) for comparing 2- vs. 0-back over 30 s of data, (C) for comparing 2- vs. 1-back (120 s of data), (D) for comparing 1- vs. 0-back (120 s of data).
Mentions: Figure 3 shows the results for different (combinations of) sensor groups of SVM and elastic net, as well as the outcome of combining the outputs of different elastic net models (“decision level”). Shown are the results for the default case of classifying 2 vs. 0-back over 2 min. data segments (a) as well as for more difficult cases: using 30 s data segments (b), or classifying 2 vs. 1-back (c), or 1 vs. 0-back (d). Comparing performance in the default case (Figure 3A) with that of separate sensors (Figure 2) shows that for Physiology, combining the three sensors leads to a (non-significant) increase in performance (accuracy of 0.75 for SVM, compared to 0.70 for the best performing single physiological sensor respiration). Models that include EEG perform significantly better than physiology and eye models (SVM, pairwise comparisons, p < 0.05). Adding sensor groups to the already well performing EEG improves classification accuracy by 3–5% (for adding Physiology or Eye variables with elastic net). For SVM, the combination of physiology and eye measures tends to improve performance relative to either one alone by 7% as well. However, all of these improvements do not reach statistical significance. Using the assumption of a binomial distribution, significance (p < 0.05) is reached for differences of around 10%.

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.