Mental Workload Estimation using Wireless EEG Signals

Previous studies have demonstrated the applicability of electroencephalogram (EEG) in estimating mental workload. However, developing reliable models for cross-task, cross-subject and cross-session classifications of workload remains a challenge. In this study, we used a wireless Emotiv EPOC headset to evaluate workload in eight subjects and two mental tasks, namely n-back, and arithmetic tasks. 0-back and 2-back tasks, and 1-digit and 3-digit additions were employed as low and high workloads in the n-back and arithmetic tasks, respectively. Using power spectral density as features, a signal processing and feature extraction framework was developed to classify workload levels. Within-session accuracies of 98.5% and 95.5% were achieved in the n-back and arithmetic tasks, respectively. To facilitate real-time estimation of workload, a fast domain adaptation technique was applied to achieve a cross-task accuracy of 68.6%. Similarly, we obtained accuracies of 80.5% and 76.6% across sessions, and 74.4% and 64.1% across subjects, in n-back and arithmetic tasks, respectively. Although the number of participants is limited, this framework generalised well across subjects and tasks, and provides a promising approach towards developing subject and task-independent models. It also shows the feasibility of using a consumer-level wireless EEG headset in cognitive monitoring for realtime estimation of workload in practice.