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Novel Classification System for Classifying Cognitive Workload Levels under Vague Visual Stimulation
Mahmoud, Rwan Adil Osman Shanableh, Tamer Bodala, Indu P. Thakor, Nitish V. Al-Nashash, Hasan
Mahmoud, Rwan Adil Osman
Shanableh, Tamer
Bodala, Indu P.
Thakor, Nitish V.
Al-Nashash, Hasan
Date
2017-07
Advisor
Type
Article
Postprint
Peer-Reviewed
Postprint
Peer-Reviewed
Degree
Citations
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Description
Abstract
This paper presents a novel method for classifying four different levels of cognitive workload. The workload levels are generated using visual stimuli degradation. 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 to the existing work, we show that the proposed solution is more accurate and computationally less demanding.