Wearable EEG-Based Cognitive Load Classification by Personalized and Generalized Model Using Brain Asymmetry

Sidratul Moontaha, Arpita Kappattanavar, Pascal Hecker, Pascal Hecker, Bert Arnrich

2023

Abstract

EEG measures have become prominent with the increasing popularity of non-invasive, portable EEG sensors for neuro-physiological measures to assess cognitive load. In this paper, utilizing a four-channel wearable EEG device, the brain activity data from eleven participants were recorded while watching a relaxation video and performing three cognitive load tasks. The data was pre-processed using outlier rejection based on a movement filter, spectral filtering, common average referencing, and normalization. Four frequency-domain feature sets were extracted from 30-second windows encompassing the power of δ, θ, α, β and γ frequency bands, the respective ratios, and the asymmetry features of each band. A personalized and generalized model was built for the binary classification between the relaxation and cognitive load tasks and self-reported labels. The asymmetry feature set outperformed the band ratio feature sets with a mean classification accuracy of 81.7% for the personalized model and 78% for the generalized model. A similar result for the models from the self-reported labels necessitates utilizing asymmetry features for cognitive load classification. Extracting high-level features from asymmetry features in the future may surpass the performance. Moreover, the better performance of the personalized model leads to future work to update pre-trained generalized models on personal data.

Paper Citation

in Harvard Style

Moontaha S., Kappattanavar A., Hecker P. and Arnrich B. (2023). Wearable EEG-Based Cognitive Load Classification by Personalized and Generalized Model Using Brain Asymmetry. In Proceedings of the 16th International Joint Conference on Biomedical Engineering Systems and Technologies (BIOSTEC 2023) - Volume 5: HEALTHINF; ISBN 978-989-758-631-6, SciTePress, pages 41-51. DOI: 10.5220/0011628300003414

in Bibtex Style

@conference{healthinf23,
author={Sidratul Moontaha and Arpita Kappattanavar and Pascal Hecker and Bert Arnrich},
title={Wearable EEG-Based Cognitive Load Classification by Personalized and Generalized Model Using Brain Asymmetry},
booktitle={Proceedings of the 16th International Joint Conference on Biomedical Engineering Systems and Technologies (BIOSTEC 2023) - Volume 5: HEALTHINF},
year={2023},
pages={41-51},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0011628300003414},
isbn={978-989-758-631-6},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 16th International Joint Conference on Biomedical Engineering Systems and Technologies (BIOSTEC 2023) - Volume 5: HEALTHINF
TI - Wearable EEG-Based Cognitive Load Classification by Personalized and Generalized Model Using Brain Asymmetry
SN - 978-989-758-631-6
AU - Moontaha S.
AU - Kappattanavar A.
AU - Hecker P.
AU - Arnrich B.
PY - 2023
SP - 41
EP - 51
DO - 10.5220/0011628300003414
PB - SciTePress