Wavelet Based Feature Extraction for Multi-Model Ensemble Approach for Mental Workload Classification Using EEG

Fiza Parveen, Arnav Bhavsar

2024

Abstract

Mental workload is a crucial aspect of cognitive processing as it reflects how much of our working memory is engaged. Studying n-back tasks of varying complexity, has been a popular way to explore the relationship between mental workload and EEG patterns. However there is still scope of improvement in achieving good performance in such a mapping. In this work, we address the classification of EEG patterns corresponding to different n-back tasks. We use publicly available n-back dataset, comprising 0-back, 2-back, and 3-back tasks to represent low, medium, and high levels of mental workload, respectively. We use wavelet-based signal decomposition technique to compute multi-resolution representation having both time and frequency patterns. This is followed by extracting a variety of hand crafted feature. We train different XGBoost models for two level and three level mental workload classification. Furthermore, we employ ensemble techniques at different levels to better categorize EEG signals. Our approach also involves finding channels that are most significant for classification of highly complex 2-back and 3-back task EEG data.

Paper Citation

in Harvard Style

Parveen F. and Bhavsar A. (2024). Wavelet Based Feature Extraction for Multi-Model Ensemble Approach for Mental Workload Classification Using EEG. In Proceedings of the 17th International Joint Conference on Biomedical Engineering Systems and Technologies - Volume 1: BIOSIGNALS; ISBN 978-989-758-688-0, SciTePress, pages 770-777. DOI: 10.5220/0012381300003657

in Bibtex Style

@conference{biosignals24,
author={Fiza Parveen and Arnav Bhavsar},
title={Wavelet Based Feature Extraction for Multi-Model Ensemble Approach for Mental Workload Classification Using EEG},
booktitle={Proceedings of the 17th International Joint Conference on Biomedical Engineering Systems and Technologies - Volume 1: BIOSIGNALS},
year={2024},
pages={770-777},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0012381300003657},
isbn={978-989-758-688-0},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 17th International Joint Conference on Biomedical Engineering Systems and Technologies - Volume 1: BIOSIGNALS
TI - Wavelet Based Feature Extraction for Multi-Model Ensemble Approach for Mental Workload Classification Using EEG
SN - 978-989-758-688-0
AU - Parveen F.
AU - Bhavsar A.
PY - 2024
SP - 770
EP - 777
DO - 10.5220/0012381300003657
PB - SciTePress