Silabdi, Hana and Hassan, Raini and Faizabadi, Ahmed Rimaz and Gubbi, Abdullah and Bellary, Mohammed Zakir and Manjula, V (2025) Enhanced EEGNet optimization using lightweight Deep Neural Network for detecting human stress levels from Raw EEG signals dataset. In: 3rd International Conference on Integrated Circuits and Communication Systems (ICICACS), Raichur, India.
|
PDF
- Published Version
Download (436kB) | Preview |
Abstract
Accurate stress detection is essential in neuroscience and healthcare since human stress levels have a substantial impact on general well-being. Although EEG-based stress detection is a popular non-invasive method, real-time applications are limited by the high processing costs of classic feature extraction techniques. This work removes the requirement for intricate feature extraction by presenting an efficient EEGNet framework for real-time stress prediction utilizing raw EEG signals. The suggested approach improves accuracy and efficiency by utilizing Z-score normalization and adjusting batch size. Compared to traditional CNNs, the lightweight EEGNet architecture's depth-wise and separable convolutions ensure computational efficiency by reducing trainable parameters by a factor of ten. The proposed method outperformed current EEGNet-based techniques in stress identification by 5% to 18%. Furthermore, the improved system efficiently handles raw EEG signals, which is suitable for real- time, resource-constrained scenarios. The results shows the effectiveness of model centric optimization in deep learning and open the door to the creation of more effective stress detection techniques. This work provides a workable solution for real- time non-invasive stress monitoring, highlighting the significance of lightweight frameworks customized for particular applications.
| Item Type: | Proceeding Paper (Invited Papers) |
|---|---|
| Additional Information: | 4964/121018 |
| Uncontrolled Keywords: | raw EEG signal, stress prediction, EEGNet, non-invasive stress monitoring, 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 |
| Depositing User: | Dr. Raini Hassan |
| Date Deposited: | 13 May 2025 12:50 |
| Last Modified: | 13 May 2025 12:50 |
| URI: | http://irep.iium.edu.my/id/eprint/121018 |
Actions (login required)
 |
View Item |
Download Statistics
Download Statistics