Unified CNN-Transformer Model for Mental Workload Classification Using EEG

Fiza Parveen, Arnav Bhavsar

2025

Abstract

The cognitive effort required for tasks requiring memory, attention, and decision-making is referred to as mental workload. Preventing cognitive overload and increasing task efficiency rely on a reliable assessment of mental workload. In this study, we present a CNN-Transformers hybrid model that uses EEG data for multi-level Mental Workload classification. Our model uses 1D-CNN to extract spatial features from windowed EEG signals followed by Transformers to capture temporal correlation.This combination improves our comprehension of mental workload situations by capturing local spatial and both long-range temporal aspects. We use a majority voting technique on the window based predictions to increase prediction reliability, making sure the final accuracy represents a thorough assessment of mental workload at signal level. A rigorous 5-fold cross-validation technique is used to evaluate the model on publicaly available STEW dataset.

Paper Citation

in Harvard Style

Parveen F. and Bhavsar A. (2025). Unified CNN-Transformer Model for Mental Workload Classification Using EEG. In Proceedings of the 18th International Joint Conference on Biomedical Engineering Systems and Technologies - Volume 1: BIOSIGNALS; ISBN 978-989-758-731-3, SciTePress, pages 928-934. DOI: 10.5220/0013190900003911

in Bibtex Style

@conference{biosignals25,
author={Fiza Parveen and Arnav Bhavsar},
title={Unified CNN-Transformer Model for Mental Workload Classification Using EEG},
booktitle={Proceedings of the 18th International Joint Conference on Biomedical Engineering Systems and Technologies - Volume 1: BIOSIGNALS},
year={2025},
pages={928-934},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0013190900003911},
isbn={978-989-758-731-3},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 18th International Joint Conference on Biomedical Engineering Systems and Technologies - Volume 1: BIOSIGNALS
TI - Unified CNN-Transformer Model for Mental Workload Classification Using EEG
SN - 978-989-758-731-3
AU - Parveen F.
AU - Bhavsar A.
PY - 2025
SP - 928
EP - 934
DO - 10.5220/0013190900003911
PB - SciTePress