Classification of Cognitive Workload Levels under Vague Visual Stimulation

Abstract

In most applications where humans are involved, it is important to augment the interaction between users and the components of these applications. One significant element is the cognitive state of the subjects involved. The cognitive state can be manipulated by the amount of cognitive workload allocated to the working memory. If the assigned cognitive workload is too low, the subject's cognition will be underutilized. In contrast, if the workload is more than the subject's capabilities, he or she will be mentally overloaded. Thus, there is a serious need to accurately assess and quantify cognitive workload levels.In this work, a method for separating four different cognitive workload levels is presented. We use an existing data set that contains EEG signals recorded from sixteen subjects while experiencing four different levels of cognitive workload. Some of these workload levels is due to the degradation of visual stimuli. The proposed solution integrates preprocessing of EEG signals, feature extraction based on discrete wavelet transform and statistical features, dimensionality reduction using stepwise regression and multiclass linear classification. Experimental results show that the average classification accuracy of the presented method is 93.4%. The effect of EEG channel selection on the classification accuracy is also investigated. The results show that channels included in the brain frontal lobes are important in cognitive workload classification. By utilizing only 23 channels, most of them are located in the frontal region; the proposed solution provides an average classification accuracy of 91%. It is shown that the proposed solution is more accurate and computationally less demanding when compared to the existing work.