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Brain-inspired signal processing for detecting stress during mental arithmetic tasks

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Publié dans:Brain Informatics vol. 12, no. 1 (Dec 2025), p. 34
Auteur principal: Belwafi, Kais
Autres auteurs: Alsuwaidi, Ahmed, Mejri, Sami, Djemal, Ridha
Publié:
Springer Nature B.V.
Sujets:
Physiology
Machine learning
Accuracy
Signal analysis
Deep learning
Signal processing
Human-computer interface
Arithmetic
Brain research
Sensors
Classification
Wearable technology
Frequencies
Neurosciences
Electroencephalography
Cognitive load
Algorithms
Mental health
Real time
Post traumatic stress disorder
Heart rate
Accès en ligne:Citation/Abstract
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Résumé: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.
ISSN:2198-4018
2198-4026
DOI:10.1186/s40708-025-00281-y
Source:Advanced Technologies & Aerospace Database