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EEG-based harmful brain activity classification using deep learning and feature fusion.

Overview

Authors: Zaib Unnisa1,2,3, Arfan Jaffar1,2, Sheeraz Akram4, IrfanUd Din3, Khalil Khan5, Sohail Masood Bhatti1,2
ORCID iDs: Zaib Unnisa, Khalil Khan
  1. Department of Computer Science, Superior University,Lahore, 54600 Pakistan
  2. Intelligent Data Visual Computing Research (IDVCR), Lahore, 54600 Pakistan
  3. Department of Computer Science, New Uzbekistan University,100000 Tashkent, Uzbekistan
  4. Information Systems Department, College of Computer and Information Sciences, Imam Mohammad Ibn Saud Islamic University (IMSIU),Riyadh, Saudi Arabia
  5. Department of Information Technology, College of Computer, Qassim University,Buraydah, Saudi Arabia
Institutions: Superior University (Pakistan); New Uzbekistan University (Uzbekistan); Imam Mohammad ibn Saud Islamic University (Saudi Arabia); Qassim University (Saudi Arabia)
Journal: Scientific reports, volume 16, issue 1, article 20817
Dates: received 3 November 2025; accepted 9 April 2026; published online 6 May 2026
Type: Research article · Language: English
License: CC BY-NC-ND
Identifiers: DOI 10.1038/s41598-026-48670-5 · PMID 42092079 · PMCID PMC13338169 · OpenAlex W7160418935
Open access: gold, a free copy (OpenAlex)
Status: data only
Categories: EEG (modality), human (organism), epilepsy (population), methods / tools (subfield)
Methods: Spectral & time-frequency
Keywords: Deep learning, Harmful brain activity, EEG signals, Feature fusion, Convolutional neural network, Computational biology and bioinformatics, Diseases, Engineering, Mathematics and computing, Neurology
MeSH: Brain*, Deep Learning*, Electroencephalography*, Seizures*, Classification Algorithms, Convolutional Neural Networks, Humans (* major topic)
Topic: EEG and Brain-Computer Interfaces (Cognitive Neuroscience, Neuroscience), according to OpenAlex
Funding: Qassim University (QU- APC-2026)
Citations: not cited yet (Europe PMC); 60 references in the paper

Abstract

The abstract is not reproduced here: the paper's license (CC BY-NC-ND) does not allow it. Read it in the paper, at the publisher or on Europe PMC.

Code

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Data

Datasets cited

Data availability statement

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Read it in the paper: doi.org/10.1038/s41598-026-48670-5.

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 2, 28 September 2026

  • Funding: added Qassim University: QU- APC-2026

Version 1, 28 September 2026: the first record

Recorded: type, language, journal, volume, issue, pages, dates, 6 authors, 10 keywords, 7 MeSH terms, 41 references.

Cite

This paper

Unnisa, Z., Jaffar, A., Akram, S., Din, I., Khan, K., & Masood Bhatti, S. (2026). EEG-based harmful brain activity classification using deep learning and feature fusion. Scientific reports, 16(1), 20817. https://doi.org/10.1038/s41598-026-48670-5

BibTeX

@article{unnisa2026eeg,
author = {Unnisa, Zaib and Jaffar, Arfan and Akram, Sheeraz and Din, IrfanUd and Khan, Khalil and Masood Bhatti, Sohail},
title = {{EEG-based harmful brain activity classification using deep learning and feature fusion}},
journal = {Scientific reports},
year = {2026},
month = may,
volume = {16},
number = {1},
pages = {20817},
publisher = {Nature Publishing Group},
issn = {2045-2322},
doi = {10.1038/s41598-026-48670-5},
url = {https://doi.org/10.1038/s41598-026-48670-5},
pmid = {42092079},
pmcid = {PMC13338169}
}

RIS

TY - JOUR
AU - Unnisa, Zaib
AU - Jaffar, Arfan
AU - Akram, Sheeraz
AU - Din, IrfanUd
AU - Khan, Khalil
AU - Masood Bhatti, Sohail
TI - EEG-based harmful brain activity classification using deep learning and feature fusion
T2 - Scientific reports
J2 - Sci Rep
PY - 2026
DA - 2026/05/06
VL - 16
IS - 1
SP - 20817
SN - 2045-2322
PB - Nature Publishing Group
DO - 10.1038/s41598-026-48670-5
UR - https://doi.org/10.1038/s41598-026-48670-5
LA - en
ER -

CSL-JSON

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"language": "en",
"issued": {
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