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Updated: Dec 11, 2025

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

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Published on: June 16, 2018

Modified Support Vector Machine for Detecting Stress Level Using EEG Signals.

Richa Gupta¹, M Afshar Alam¹, Parul Agarwal¹

¹Department of Computer Science and Engineering, School of Engineering and Technology, Jamia Hamdard, Delhi 110062, India.

Computational Intelligence and Neuroscience

|August 18, 2020

Summary

This summary is machine-generated.

This study introduces a novel algorithm combining five methods to accurately detect mental stress from EEG signals. The enhanced approach shows superior performance, aiding mental health professionals in stress diagnosis.

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Psychophysiological Stress Assessment Using Biofeedback

Psychophysiological Stress Assessment Using Biofeedback

Published on: July 31, 2009

Area of Science:

Neuroscience
Computational Intelligence
Biomedical Engineering

Background:

Mental stress is a significant health concern, often stemming from the human brain's response to demanding situations.
Accurate detection of mental stress is crucial for timely intervention and management by healthcare professionals.
Existing methods for stress detection using electroencephalogram (EEG) signals have limitations in accuracy and efficiency.

Purpose of the Study:

To develop and validate an enhanced algorithm for accurate mental stress detection using EEG signals.
To improve the precision of stress level identification by integrating multiple signal processing and machine learning techniques.
To provide a robust tool for psychiatrists and health consultants to aid in diagnosing stress.

Main Methods:

A fusion of five algorithms was employed, including modified Whale Optimization Algorithm for Support Vector Machine (SVM) kernel selection.
Non-Local Means (NLM), Discrete Cosine Transform (DCT), and Multi-Band Particle Swarm Optimization (MBPSO) were utilized for preprocessing, feature extraction, and selection.
The proposed algorithm was tested on EEG data collected from 14 subjects.

Main Results:

The integrated algorithm achieved high performance metrics: 96.36% accuracy, 96.84% sensitivity, 90.8% specificity, and 97.96% F1 score.
The proposed method demonstrated superior performance compared to existing algorithms for mental stress detection.
Validation on real-world EEG data confirmed the algorithm's effectiveness in identifying stress levels.

Conclusions:

The developed algorithm offers a significant advancement in the accurate and reliable detection of mental stress from EEG signals.
The fusion of advanced algorithms provides a powerful tool for objective stress assessment.
This approach holds promise for practical application in clinical settings for mental health diagnosis and management.