Leveraging EEG and Signal-to-Noise Ratio augmentation for advanced stress detection

Silabdi, Hana and Hassan, Raini and Faizabadi, Ahmed Rimaz and Gubbi, Abdullah and Bellary, Mohammed Zakir and M, Afsar Baig (2025) Leveraging EEG and Signal-to-Noise Ratio augmentation for advanced stress detection. In: 2025 4th International Conference on Distributed Computing and Electrical Circuits and Electronics (ICDCECE), 25 - 26 April 2025, Ballari.

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Abstract

Student stress has emerged as a significant concern, requiring prompt identification to avoid serious repercussions. Unlike conventional EEG stress detection approaches that rely on feature extraction, our work introduces a novel combination of SNR-based augmentation with ShallowConvNet recognised for its simplicity and efficiency. Utilising the StressDB-UIA1 dataset, EEG data from 31 subjects were examined under stress and non-stress situations. The research tackles the issue of restricted EEG data availability by utilising Signal-to-Noise Ratio (SNR) based augmentation, replicating noise levels of 10 dB, 15 dB, and 20 dB. This augmentation strategy improves model robustness and generalisability to real-world situations. The results shows that ShallowConvNet, when trained on SNR-augmented datasets, attains enhanced accuracy and Area Under Curve (AUC) metrics, with peak performance recorded at 20 dB SNR (83.69% accuracy, 0.921 AUC). SNR-based augmentation is apparent in enhancing EEG classification and emphasise ShallowConvNet's capability for real-time stress monitoring, facilitating prompt interventions and mental health support systems.

Item Type:	Proceeding Paper (Other)
Uncontrolled Keywords:	raw EEG signal, electroencephalography, stress prediction, shallowconvnet, non-invasive stress monitoring, signal-to-noise ratio, augmentation, dataset stressDB-UIA1
Subjects:	Q Science > QA Mathematics > QA75 Electronic computers. Computer science
Kulliyyahs/Centres/Divisions/Institutes (Can select more than one option. Press CONTROL button):	Kulliyyah of Information and Communication Technology > Department of Computer Science Kulliyyah of Information and Communication Technology > Department of Computer Science Kulliyyah of Information and Communication Technology Kulliyyah of Information and Communication Technology
Depositing User:	Dr. Raini Hassan
Date Deposited:	04 Jul 2025 16:05
Last Modified:	04 Jul 2025 16:05
URI:	http://irep.iium.edu.my/id/eprint/121868

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