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

Overview

Authors: Usman Rauf1, Anfal Zahid2, Amina Qadeer2, Adeel Zafar3, Shehrayar Khan2, Sanay Muhammad Umer Saeed2
ORCID iDs: Adeel Zafar
  1. Computer Engineering Department, HITEC University,Taxila, Pakistan
  2. Computer Engineering Department, University of Engineering and Technology, Taxila, Pakistan
  3. Computer Engineering Department, Halmstad University,Halmstad, Sweden
Institutions: HITEC University (Pakistan); University of Engineering and Technology Taxila (Pakistan); Halmstad University (Sweden)
Journal: Scientific reports, volume 16, issue 1, article 16568
Dates: received 31 December 2025; accepted 23 April 2026; published online 28 May 2026
Type: Research article · Language: English
License: CC BY
Identifiers: DOI 10.1038/s41598-026-50857-9 · PMID 42209559 · PMCID PMC13219437 · OpenAlex W7162652297
Open access: gold, a free copy (OpenAlex)
Status: data only
Categories: EEG (modality), human (organism), methods / tools (subfield)
Methods: Machine learning, Preprocessing, Evoked potentials, Connectivity, Physiology & signal measures
Keywords: Time Domain analysis, Feature selection, Electroencephalography (EEG), Perceived stress, Segmentation technique, Computational biology and bioinformatics, Engineering, Mathematics and computing, Neuroscience
MeSH: Electroencephalography*, Stress, Psychological*, Algorithms, Classification Algorithms, Humans, Signal Processing, Computer-Assisted, Subjective Stress (* major topic)
Topic: Emotion and Mood Recognition (Experimental and Cognitive Psychology, Psychology), according to OpenAlex
Funding: Halmstad University
Citations: not cited yet (Europe PMC); 54 references in the paper

Abstract

Stress has been recognized as a significant global health issue, affecting the majority of the population. Rapid and accurate detection of stress is critical for stress treatment. A considerable part of prior work has focused on classifying electroencephalography signals to enable preliminary detection of stress. Previous work has demonstrated considerable success in stress classification/detection using electroencephalography signals. The proposed work aims to classify human stress using electroencephalography signals to enable early intervention. In this research, we have worked with a dataset comprising nearly 211 individuals and proposed a method based on time-domain analysis. Segmentation techniques are used to analyze stress from EEG signals. Both overlapping and non-overlapping methods are employed in this research work. The EEG signals last approximately 480 seconds. We have used the Perceived Stress Questionnaire (PSQ) for the labeled classes of ‘stressed’ and ‘non-stressed’. Different classifiers have been employed to distinguish between stressed and non-stressed classes. Our proposed method achieved an accuracy of 96.32% using a K-nearest neighbors classifier with a non-overlapping segmentation technique.

Reproduced under the paper's license (CC BY), from the paper cited above.

Code

The paper links to its data, not to its authors' code: see the Data section.

Tracing map

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Data

Datasets cited

Data availability

To support reproducibility and future research, the following resources have been made publicly available: The pre-processed EEG recordings from four electrodes, AF7, AF8, TP9, and TP10. The full set of extracted feature vectors and the computed engagement indices derived from these signals. All data are provided in CSV format and are available at: https://www.kaggle.com/datasets/usmanrauf/timedomain-segmentation-lemon.

Reproduced under the paper's license (CC BY), from the paper cited above.

Versions

The history of this record: each version stored by the harvester or made by a correction of its authors or of the maintainers of its code, and what changed in its facts. The texts of the paper (its abstract, its availability statements) are not part of it; versions that changed only those are not listed.

Version 1, 28 September 2026: the first record

Recorded: type, language, journal, volume, issue, pages, dates, 6 authors, 9 keywords, 7 MeSH terms, 1 funder, 38 references.

Cite

This paper

Rauf, U., Zahid, A., Qadeer, A., Zafar, A., Khan, S., & Saeed, S. M. U. (2026). EEG-based stress classification using time-domain features and segmentation techniques. Scientific reports, 16(1), 16568. https://doi.org/10.1038/s41598-026-50857-9

BibTeX

@article{rauf2026eeg,
author = {Rauf, Usman and Zahid, Anfal and Qadeer, Amina and Zafar, Adeel and Khan, Shehrayar and Saeed, Sanay Muhammad Umer},
title = {{EEG-based stress classification using time-domain features and segmentation techniques}},
journal = {Scientific reports},
year = {2026},
month = may,
volume = {16},
number = {1},
pages = {16568},
publisher = {Nature Publishing Group},
issn = {2045-2322},
doi = {10.1038/s41598-026-50857-9},
url = {https://doi.org/10.1038/s41598-026-50857-9},
pmid = {42209559},
pmcid = {PMC13219437}
}

RIS

TY - JOUR
AU - Rauf, Usman
AU - Zahid, Anfal
AU - Qadeer, Amina
AU - Zafar, Adeel
AU - Khan, Shehrayar
AU - Saeed, Sanay Muhammad Umer
TI - EEG-based stress classification using time-domain features and segmentation techniques
T2 - Scientific reports
J2 - Sci Rep
PY - 2026
DA - 2026/05/28
VL - 16
IS - 1
SP - 16568
SN - 2045-2322
PB - Nature Publishing Group
DO - 10.1038/s41598-026-50857-9
UR - https://doi.org/10.1038/s41598-026-50857-9
LA - en
ER -

CSL-JSON

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