Performance Evaluation of EEG Based Mental Stress Assessment Approaches for Wearable Devices

Ubaid M Al-Saggaf; Syed Faraz Naqvi; Muhammad Moinuddin; Sulhi Ali Alfakeh; Syed Saad Azhar Ali

doi:10.3389/fnbot.2021.819448

Performance Evaluation of EEG Based Mental Stress Assessment Approaches for Wearable Devices

Front Neurorobot. 2022 Feb 4:15:819448. doi: 10.3389/fnbot.2021.819448. eCollection 2021.

Authors

Ubaid M Al-Saggaf^{1

2}, Syed Faraz Naqvi³, Muhammad Moinuddin^{1

2}, Sulhi Ali Alfakeh⁴, Syed Saad Azhar Ali³

Affiliations

¹ Electrical and Computer Engineering Department, King Abdulaziz University, Jeddah, Saudi Arabia.
² Center of Excellence in Intelligent Engineering Systems (CEIES), King Abdulaziz University, Jeddah, Saudi Arabia.
³ Department of Electrical & Electronics Engineering, Center for Intelligent Signal and Imaging Research, Universiti Teknologi PETRONAS, Bandar Seri Iskandar, Malaysia.
⁴ Department of Internal Medicine, Child and Adolescent Psychiatrist, Faculty of Medicine, King Abdulaziz University, Jeddah, Saudi Arabia.

Abstract

Mental stress has been identified as the root cause of various physical and psychological disorders. Therefore, it is crucial to conduct timely diagnosis and assessment considering the severe effects of mental stress. In contrast to other health-related wearable devices, wearable or portable devices for stress assessment have not been developed yet. A major requirement for the development of such a device is a time-efficient algorithm. This study investigates the performance of computer-aided approaches for mental stress assessment. Machine learning (ML) approaches are compared in terms of the time required for feature extraction and classification. After conducting tests on data for real-time experiments, it was observed that conventional ML approaches are time-consuming due to the computations required for feature extraction, whereas a deep learning (DL) approach results in a time-efficient classification due to automated unsupervised feature extraction. This study emphasizes that DL approaches can be used in wearable devices for real-time mental stress assessment.

Keywords: computer-aided diagnosis (CAD); convolutional neural network; feature extraction; machine learning; real time; rehabilitation; sliding window; stress-assessment.