Brain-inspired signal processing for detecting stress during mental arithmetic tasks
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| Publicat a: | Brain Informatics vol. 12, no. 1 (Dec 2025), p. 34 |
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| Autor principal: | |
| Altres autors: | , , |
| Publicat: |
Springer Nature B.V.
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| Matèries: | |
| Accés en línia: | Citation/Abstract Full Text Full Text - PDF |
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MARC
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| 001 | 3280693378 | ||
| 003 | UK-CbPIL | ||
| 022 | |a 2198-4018 | ||
| 022 | |a 2198-4026 | ||
| 024 | 7 | |a 10.1186/s40708-025-00281-y |2 doi | |
| 035 | |a 3280693378 | ||
| 045 | 2 | |b d20251201 |b d20251231 | |
| 100 | 1 | |a Belwafi, Kais |u University of Sharjah, Department of Computer Engineering, College of Computing and informatics, Sharjah, United Arab Emirates (GRID:grid.412789.1) (ISNI:0000 0004 4686 5317) | |
| 245 | 1 | |a Brain-inspired signal processing for detecting stress during mental arithmetic tasks | |
| 260 | |b Springer Nature B.V. |c Dec 2025 | ||
| 513 | |a Journal Article | ||
| 520 | 3 | |a 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. | |
| 653 | |a Physiology | ||
| 653 | |a Machine learning | ||
| 653 | |a Accuracy | ||
| 653 | |a Signal analysis | ||
| 653 | |a Deep learning | ||
| 653 | |a Signal processing | ||
| 653 | |a Human-computer interface | ||
| 653 | |a Arithmetic | ||
| 653 | |a Brain research | ||
| 653 | |a Sensors | ||
| 653 | |a Classification | ||
| 653 | |a Wearable technology | ||
| 653 | |a Frequencies | ||
| 653 | |a Neurosciences | ||
| 653 | |a Electroencephalography | ||
| 653 | |a Cognitive load | ||
| 653 | |a Algorithms | ||
| 653 | |a Mental health | ||
| 653 | |a Real time | ||
| 653 | |a Post traumatic stress disorder | ||
| 653 | |a Heart rate | ||
| 700 | 1 | |a Alsuwaidi, Ahmed |u University of Sharjah, Department of Computer Engineering, College of Computing and informatics, Sharjah, United Arab Emirates (GRID:grid.412789.1) (ISNI:0000 0004 4686 5317) | |
| 700 | 1 | |a Mejri, Sami |u Khalifa University of Science and Technology, Pedagogical Enhancement - CTL, Abu Dhabi, United Arab Emirates (GRID:grid.440568.b) (ISNI:0000 0004 1762 9729) | |
| 700 | 1 | |a Djemal, Ridha |u National School of Engineering, University of Sfax, Department of Electrical Engineering, ATMS Lab, Sfax, Tunisia (GRID:grid.412124.0) (ISNI:0000 0001 2323 5644) | |
| 773 | 0 | |t Brain Informatics |g vol. 12, no. 1 (Dec 2025), p. 34 | |
| 786 | 0 | |d ProQuest |t Advanced Technologies & Aerospace Database | |
| 856 | 4 | 1 | |3 Citation/Abstract |u https://www.proquest.com/docview/3280693378/abstract/embedded/L8HZQI7Z43R0LA5T?source=fedsrch |
| 856 | 4 | 0 | |3 Full Text |u https://www.proquest.com/docview/3280693378/fulltext/embedded/L8HZQI7Z43R0LA5T?source=fedsrch |
| 856 | 4 | 0 | |3 Full Text - PDF |u https://www.proquest.com/docview/3280693378/fulltextPDF/embedded/L8HZQI7Z43R0LA5T?source=fedsrch |