Mental Workload Estimation using Wireless EEG Signals

Quadri Adewale, George Panoutsos

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 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 real-time estimation of workload in practice.

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Paper Citation


in Harvard Style

Adewale Q. and Panoutsos G. (2021). Mental Workload Estimation using Wireless EEG Signals.In Proceedings of the 14th International Joint Conference on Biomedical Engineering Systems and Technologies - Volume 2: BIOSIGNALS, ISBN 978-989-758-490-9, pages 200-207. DOI: 10.5220/0010251302000207


in Bibtex Style

@conference{biosignals21,
author={Quadri Adewale and George Panoutsos},
title={Mental Workload Estimation using Wireless EEG Signals},
booktitle={Proceedings of the 14th International Joint Conference on Biomedical Engineering Systems and Technologies - Volume 2: BIOSIGNALS,},
year={2021},
pages={200-207},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0010251302000207},
isbn={978-989-758-490-9},
}


in EndNote Style

TY - CONF

JO - Proceedings of the 14th International Joint Conference on Biomedical Engineering Systems and Technologies - Volume 2: BIOSIGNALS,
TI - Mental Workload Estimation using Wireless EEG Signals
SN - 978-989-758-490-9
AU - Adewale Q.
AU - Panoutsos G.
PY - 2021
SP - 200
EP - 207
DO - 10.5220/0010251302000207