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ORIGINAL RESEARCH article

Front. Comput. Neurosci.
Volume 18 - 2024 | doi: 10.3389/fncom.2024.1482994
This article is part of the Research Topic Advancements in Smart Diagnostics for Understanding Neurological Behaviors and Biosensing Applications View all articles

BrainNet: An Automated Approach for Brain Stress Prediction Utilizing Electrodermal Activity Signal With XLNet Model

Provisionally accepted
Liao Xuanzhi Liao Xuanzhi 1Abeer M. Hakeem Abeer M. Hakeem 2Linda Mohaisen Linda Mohaisen 2Muhammad Umer Muhammad Umer 3*Muhammad Attique Khan Muhammad Attique Khan 4Shrooq Alsenan Shrooq Alsenan 5Nisreen Innab Nisreen Innab 6*
  • 1 Beibu Gulf University, Qinzhou, China
  • 2 Department of Information Technology, Faculty of Computing and Information Technology, King Abdulaziz University, Jeddah, Makkah, Saudi Arabia
  • 3 Islamia University of Bahawalpur, Bahawalpur, Pakistan
  • 4 Prince Mohammad bin Fahd University, Khobar, Saudi Arabia
  • 5 Department of Information Systems, College of Computer and Information Sciences, Princess Nourah bint Abdulrahman University, Riyadh, Saudi Arabia
  • 6 Department of Computer Science and Information Systems, College of Applied Sciences, University of Almaarefa, Dariyah, Riyadh, Saudi Arabia

The final, formatted version of the article will be published soon.

    Brain stress monitoring has emerged as a critical research area for understanding and managing stress and neurological health issues. This burgeoning field aims to provide accurate information and prediction about individuals' stress levels by analyzing behavioral data and physiological signals. To address this emerging problem, this research work proposes an innovative approach that uses an attention mechanism-based XLNet model (called BrainNet) for continuous stress monitoring and stress level prediction. The proposed model analyzes streams of brain data, including behavioral and physiological signal patterns using Swell and WESAD datasets. Testing on the Swell multiclass dataset, the model achieves an impressive accuracy of 95.76%. Furthermore, when evaluated on the WESAD dataset, it demonstrates even higher accuracy, reaching 98.32%. When applied to the binary classification of stress and no stress using the Swell dataset, the model achieves an outstanding accuracy of 97.19%. Comparative analysis with other previously published research works underscores the superior performance of the proposed 1 LIAO et al.approach. In addition, cross-validation confirms the significance, efficacy, and robustness of the model in brain stress level prediction and aligns with the goals of smart diagnostics for understanding neurological behaviors.

    Keywords: brain stress monitoring, XLNet, Smart healthcare, EEG monitoring, artificial intelligence, swell, WESAD

    Received: 19 Aug 2024; Accepted: 24 Sep 2024.

    Copyright: © 2024 Xuanzhi, Hakeem, Mohaisen, Umer, Khan, Alsenan and Innab. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

    * Correspondence:
    Muhammad Umer, Islamia University of Bahawalpur, Bahawalpur, Pakistan
    Nisreen Innab, Department of Computer Science and Information Systems, College of Applied Sciences, University of Almaarefa, Dariyah, 71666, Riyadh, Saudi Arabia

    Disclaimer: All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article or claim that may be made by its manufacturer is not guaranteed or endorsed by the publisher.