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Related Experiment Video

Updated: Jan 9, 2026

Evaluation of Commercial-Off-The-Shelf Wrist Wearables to Estimate Stress on Students

Evaluation of Commercial-Off-The-Shelf Wrist Wearables to Estimate Stress on Students

Published on: June 16, 2018

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

Kais Belwafi¹, Ahmed Alsuwaidi², Sami Mejri³

¹Department of Computer Engineering, College of Computing and informatics, University of Sharjah, Sharjah, United Arab Emirates. kbelwafi@sharjah.ac.ae.

Brain Informatics

|December 3, 2025

Summary

This summary is machine-generated.

This study presents a new, lightweight Brain-Computer Interface (BCI) for stress detection using ElectroEncephaloGraphy (EEG). The efficient, subject-independent method achieves high accuracy without calibration, ideal for real-time applications.

Keywords:

Brain-computer interface EEG Mental health monitoring Signal processing Stress detection

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Last Updated: Jan 9, 2026

Evaluation of Commercial-Off-The-Shelf Wrist Wearables to Estimate Stress on Students

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Published on: June 16, 2018

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Reducing State Anxiety Using Working Memory Maintenance

Reducing State Anxiety Using Working Memory Maintenance

Published on: July 19, 2017

Area of Science:

Neuroscience
Biomedical Engineering
Signal Processing

Background:

Brain-Computer Interfaces (BCIs) offer potential for stress detection and emotional resilience.
Existing methods often require subject-specific calibration and high computational resources.
Real-time, efficient stress detection is crucial for practical BCI applications.

Purpose of the Study:

To introduce a lightweight, subject-independent BCI method for real-time stress detection.
To develop a simplified ElectroEncephaloGraphy (EEG) signal analysis pipeline for stress detection.
To enable stress detection without the need for individual subject training or calibration.

Main Methods:

Utilized ElectroEncephaloGraphy (EEG) signal analysis with a simplified processing pipeline.
Preprocessed EEG data to remove artifacts and focused on alpha, beta, and gamma frequency bands.
Extracted features via band power and baseline deviation, employing statistical thresholding for stress classification.

Main Results:

Achieved an average accuracy of 88.89% on a public dataset of 36 subjects.
Demonstrated effective identification of stress-related brainwave patterns.
Validated a method suitable for low computational cost and real-time use.

Conclusions:

The developed BCI method is efficient and effective for stress detection.
Subject-independent classification enhances applicability in real-world scenarios.
The lightweight design is suitable for integration into embedded and wearable devices.