This paper presents a novel method for classifying four different levels of cognitive workload. The workload levels are generated using visual stimuli degradation. Electroencephalogram (EEG) signals recorded from 16 subjects were used for workload classification. The proposed solution includes preprocessing of EEG signals and feature extraction based on statistical features. This is followed by variable selection using stepwise regression and multiclass linear classification. The presented method achieved an average classification accuracy of 93.4%. The effect of EEG channel selection on the classification accuracy is also investigated. In comparison with the existing work, we show that the proposed solution is more accurate and computationally less demanding.
- Channel selection
- cognitive workload
- stepwise regression
ASJC Scopus subject areas
- Electrical and Electronic Engineering