Brain-inspired signal processing for detecting stress during mental arithmetic tasks
محفوظ في:
| الحاوية / القاعدة: | Brain Informatics vol. 12, no. 1 (Dec 2025), p. 34 |
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| المؤلف الرئيسي: | |
| مؤلفون آخرون: | , , |
| منشور في: |
Springer Nature B.V.
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| الموضوعات: | |
| الوصول للمادة أونلاين: | Citation/Abstract Full Text Full Text - PDF |
| الوسوم: |
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| مستخلص: | Brain-Computer Interfaces provide promising alternatives for detecting stress and enhancing emotional resilience. This study introduces a lightweight, subject-independent method for detecting stress during arithmetic tasks, designed for low computational cost and real-time use. Stress detection is performed through ElectroEncephaloGraphy (EEG) signal analysis using a simplified processing pipeline. The method begins with preprocessing the EEG recordings to eliminate artifacts and focus on relevant frequency bands (, , and ). Features are extracted by calculating band power and its deviation from a baseline. A statistical thresholding mechanism classifies stress and no-stress epochs without the need for subject-specific calibration. The approach was validated on a publicly available dataset of 36 subjects and achieved an average accuracy of 88.89%. The method effectively identifies stress-related brainwave patterns while maintaining efficiency, making it suitable for embedded and wearable devices. Unlike many existing systems, it does not require subject-specific training, enhancing its applicability in real-world environments. |
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| تدمد: | 2198-4018 2198-4026 |
| DOI: | 10.1186/s40708-025-00281-y |
| المصدر: | Advanced Technologies & Aerospace Database |