The use of eye metrics to predict the state of one's mental workload involves reliable and accurate modeling techniques. This study assessed the workload classification accuracy of three data mining techniques; artificial neural network (ANN), logistic regression, and classification tree. The results showed that the selection of model technique and the interaction between model type and time segmentation have significant effects on the ability to predict an individual's mental workload during a recall task. The ANN and classification tree both performed much better than logistic regression with 1-s incremented data. The classification tree also performed much better with data averaged over the full recall task. In addition, the transparency of the classification tree showed that pupil diameter and divergence are significantly more important predictors than fixation when modeling 1-s incremented data.
Electroencephalography (EEG) has the prospect of providing a means to gauge operator workload in a manner that does not intrude on the task being performed. Specifically, it has been proposed that the technique could be used as a method to speed the learning of a task, by adjusting the task to suit the state of the learner. The present study recorded EEG while participants performed a simulated Unmanned Aerial Vehicle (UAV) reconnaissance task. Analysis of power in three EEG frequency bands of interest found differences between the types of task being performed; however more complex analysis may be necessary to discern levels of difficulty within the task.
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