loading
Papers Papers/2022 Papers Papers/2022

Research.Publish.Connect.

Paper

Authors: Fiza Parveen and Arnav Bhavsar

Affiliation: Indian Institute of Technology Mandi, Mandi, India

Keyword(s): Mental Workload Classification, EEG, Convolutional Neural Network, Attention Mechanism, Transformers, Ensemble Majority Voting.

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.

CC BY-NC-ND 4.0

Sign In Guest: Register as new SciTePress user now for free.

Sign In SciTePress user: please login.

PDF ImageMy Papers

You are not signed in, therefore limits apply to your IP address 3.129.194.130

In the current month:
Recent papers: 100 available of 100 total
2+ years older papers: 200 available of 200 total

Paper citation in several formats:
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 - BIOSIGNALS; ISBN 978-989-758-731-3; ISSN 2184-4305, SciTePress, pages 928-934. DOI: 10.5220/0013190900003911

@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 - BIOSIGNALS},
year={2025},
pages={928-934},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0013190900003911},
isbn={978-989-758-731-3},
issn={2184-4305},
}

TY - CONF

JO - Proceedings of the 18th International Joint Conference on Biomedical Engineering Systems and Technologies - BIOSIGNALS
TI - Unified CNN-Transformer Model for Mental Workload Classification Using EEG
SN - 978-989-758-731-3
IS - 2184-4305
AU - Parveen, F.
AU - Bhavsar, A.
PY - 2025
SP - 928
EP - 934
DO - 10.5220/0013190900003911
PB - SciTePress

- Science and Technology Publications, Lda.
RESOURCES

Proceedings

Papers

Authors

Ontology

CONTACTS

Science and Technology Publications, Lda
Avenida de S. Francisco Xavier, Lote 7 Cv. C,
2900-616 Setúbal, Portugal.

Phone: +351 265 520 185 (National fixed network call)
Fax: +351 265 520 186
Email: info@scitepress.org

EXTERNAL LINKS

PRIMORIS

INSTICC

SCITEVENTS

CROSSREF

PROCEEDINGS SUBMITTED FOR INDEXATION BY:

dblp

Ei Compendex

SCOPUS

Semantic Scholar

Google Scholar

Microsoft Academic