Content area
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.
Details
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
1 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)
2 Khalifa University of Science and Technology, Pedagogical Enhancement - CTL, Abu Dhabi, United Arab Emirates (GRID:grid.440568.b) (ISNI:0000 0004 1762 9729)
3 National School of Engineering, University of Sfax, Department of Electrical Engineering, ATMS Lab, Sfax, Tunisia (GRID:grid.412124.0) (ISNI:0000 0001 2323 5644)