Authors:
Quadri Adewale
1
and
George Panoutsos
2
Affiliations:
1
Montreal Neurological Institute, McGill University, Montreal, Canada
;
2
Automatic Control & Systems Engineering, University of Sheffield, Sheffield, U.K.
Keyword(s):
Electroencephalogram (EEG), Mental Workload, Cross-task, Cross-subject, Cross-session, Wireless EEG Headset, Domain Adaptation, N-Back Task, Mental Arithmetic Task.
Abstract:
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 sessi
ons, 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 real-time estimation of workload in practice.
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