Mental Stress Classification Among Higher Education Students In Malaysia From Electroencephalogram (Eeg) Using Convolutional Neural Network With Modified Stochastic Gradient Descent. - Rashid, Nur Ramizah Ramino (2024)
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.