Rashid, Nur Ramizah Ramino
(2024)
Mental Stress Classification Among
Higher Education Students In Malaysia
From Electroencephalogram (Eeg)
Using Convolutional Neural Network
With Modified Stochastic Gradient
Descent.
PhD thesis, Universiti Sains Malaysia.
Abstract
This study investigates the classification of mental stress among Malaysian university
students using Electroencephalogram (EEG) data and a 1D-Convolutional Neural
Network (1D-CNN) optimized with Modified Stochastic Gradient Descent (SGD). The
research addresses a significant gap in the availability of localized datasets for stress
detection using EEG signals, as existing models and datasets predominantly focus on
other populations and do not account for regional variations in stressors and responses.
Moreover, there is a lack of optimization in stress detection models, specifically in
handling EEG data, which can affect the models’ accuracy and real-time application
potential. To address these challenges, EEG signals were collected during Stroop tests
and self-reported stress levels were measured using the Perceived Stress Scale (PSS).
A rigorous preprocessing approach, including Independent Component Analysis (ICA)
for artifact removal, was applied, followed by feature extraction focusing on key metrics
such as energy, entropy, and standard deviation from both time and frequency domains.
The chosen algorithm, 1D-CNN, was modified using a tailored SGD optimizer that
incorporates momentum and learning rate decay to improve convergence and address
challenges like vanishing gradients. This modification was essential for enhancing
the model’s learning process, ultimately leading to better stress classification performance.
The proposed 1D CNN model, enhanced with Modified SGD, demonstrated
superior performance compared to traditional models such as Support Vector Machines
(SVM), k-Nearest Neighbors (k-NN), and deeper architectures like Standard CNN and
AlexNet. Specifically, the 1D CNN achieved an accuracy of 92.64%, outperforming
SVM (84.5%), k-NN (76.6%), Standard CNN (91.3%), RNN (90.04%) and AlexNet
(91.65%). The 1D CNN model also demonstrated high sensitivity and specificity,
making it a robust solution for EEG-based stress detection.
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