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Related Experiment Video

Updated: May 31, 2026

STFEEG-Tool: A Spatial-Temporal-Frequency EEG Analysis Tool for Motor Imagery Brain-Computer Interfaces
05:36

STFEEG-Tool: A Spatial-Temporal-Frequency EEG Analysis Tool for Motor Imagery Brain-Computer Interfaces

Published on: March 10, 2026

EEG-based stress classification using time-domain features and segmentation techniques.

Usman Rauf1, Anfal Zahid2, Amina Qadeer2

  • 1Computer Engineering Department, HITEC University, Taxila, Pakistan. usman.rauf@hitecuni.edu.pk.

Scientific Reports
|May 28, 2026
PubMed
Summary
This summary is machine-generated.

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This study accurately detects human stress using electroencephalography (EEG) signals with 96.32% accuracy. The findings support early stress intervention through advanced signal analysis.

Area of Science:

  • Neuroscience
  • Biomedical Engineering
  • Health Informatics

Background:

  • Stress is a prevalent global health concern impacting a large portion of the population.
  • Early and precise stress detection is crucial for effective stress management and treatment.
  • Electroencephalography (EEG) signals are increasingly utilized for preliminary stress detection and classification.

Purpose of the Study:

  • To classify human stress levels using electroencephalography (EEG) signals for early intervention.
  • To evaluate the efficacy of time-domain analysis and segmentation techniques for stress detection from EEG data.
  • To compare the performance of various classifiers in distinguishing between stressed and non-stressed states based on EEG patterns.

Main Methods:

  • A dataset of EEG signals from 211 individuals was analyzed using time-domain analysis.
Keywords:
Electroencephalography (EEG)Feature selectionPerceived stressSegmentation techniqueTime Domain analysis

Related Experiment Videos

Last Updated: May 31, 2026

STFEEG-Tool: A Spatial-Temporal-Frequency EEG Analysis Tool for Motor Imagery Brain-Computer Interfaces
05:36

STFEEG-Tool: A Spatial-Temporal-Frequency EEG Analysis Tool for Motor Imagery Brain-Computer Interfaces

Published on: March 10, 2026

  • Segmentation techniques (overlapping and non-overlapping) were applied to EEG data lasting approximately 480 seconds.
  • The Perceived Stress Questionnaire (PSQ) was used to label data as 'stressed' or 'non-stressed', and K-nearest neighbors classifier was employed.

    • Main Results:

    • The proposed method achieved a high accuracy of 96.32% in classifying stressed versus non-stressed individuals.
    • Non-overlapping segmentation combined with a K-nearest neighbors classifier yielded the best performance.
    • The study demonstrates the effectiveness of EEG signal analysis for reliable stress detection.

    Conclusions:

    • Time-domain analysis of EEG signals, particularly with non-overlapping segmentation, is a highly effective method for stress classification.
    • The findings support the use of EEG-based systems for early stress detection and intervention.
    • This research contributes to the development of objective tools for mental health monitoring.