OSCR

Differential quadruple pattern: A new EEG signal classification framework.

Overview

Authors: Bilge Ozgor1, Omer Faruk Goktas2, Mehmet Baygin3, Sengul Dogan4, Turker Tuncer4
ORCID iDs: Omer Faruk Goktas
  1. Pediatric Neurology, Faculty of Medicine, Inonu University, Malatya, Türkiye
  2. Department of Electronics and Automation, Technical Sciences Vocational School, Ankara Yildirim Beyazit University, Ankara, Türkiye
  3. Department of Computer Engineering, Erzurum Technical University, Erzurum 25020, Türkiye
  4. Department of Digital Forensics Engineering, College of Technology, Firat University, Elazig 23119, Türkiye
Institutions: Inonu University (Türkiye); Ankara Yıldırım Beyazıt University (Türkiye); Erzurum Technical University (Türkiye); Fırat University (Türkiye)
Journal: IBRO neuroscience reports, volume 20, pages 492-505
Dates: received 16 January 2026; accepted 3 March 2026; published online 5 March 2026
Type: Research article · Language: English
License: CC BY-NC-ND
Identifiers: DOI 10.1016/j.ibneur.2026.03.002 · PMID 41907560 · PMCID PMC13019922 · OpenAlex W7133911652
Open access: gold, a free copy (OpenAlex)
Status: data only
Categories: EEG (modality), methods / tools (subfield)
Methods: Statistics, Machine learning, Complexity, Physiology & signal measures
Keywords: DiffQuadPat, XAI, Directed Lobish, EEG signal classification, Feature extraction
Topic: EEG and Brain-Computer Interfaces (Cognitive Neuroscience, Neuroscience), according to OpenAlex
Citations: not cited yet (Europe PMC); 39 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

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

Tracing map

A tracing map links a paper to the code its authors published: this paper has none, so it has no map.

Data

Datasets cited

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, 30 September 2026: the first record

Recorded: type, language, journal, volume, pages, dates, 5 authors, 5 keywords, 23 references.

Cite

This paper

Ozgor, B., Goktas, O. F., Baygin, M., Dogan, S., & Tuncer, T. (2026). Differential quadruple pattern: A new EEG signal classification framework. IBRO neuroscience reports, 20, 492-505. https://doi.org/10.1016/j.ibneur.2026.03.002

BibTeX

@article{ozgor2026differential,
author = {Ozgor, Bilge and Goktas, Omer Faruk and Baygin, Mehmet and Dogan, Sengul and Tuncer, Turker},
title = {{Differential quadruple pattern: A new EEG signal classification framework}},
journal = {IBRO neuroscience reports},
year = {2026},
month = mar,
volume = {20},
pages = {492--505},
publisher = {Elsevier},
issn = {2667-2421},
doi = {10.1016/j.ibneur.2026.03.002},
url = {https://doi.org/10.1016/j.ibneur.2026.03.002},
pmid = {41907560},
pmcid = {PMC13019922}
}

RIS

TY - JOUR
AU - Ozgor, Bilge
AU - Goktas, Omer Faruk
AU - Baygin, Mehmet
AU - Dogan, Sengul
AU - Tuncer, Turker
TI - Differential quadruple pattern: A new EEG signal classification framework
T2 - IBRO neuroscience reports
J2 - IBRO Neurosci Rep
PY - 2026
DA - 2026/03/05
VL - 20
SP - 492
EP - 505
SN - 2667-2421
PB - Elsevier
DO - 10.1016/j.ibneur.2026.03.002
UR - https://doi.org/10.1016/j.ibneur.2026.03.002
LA - en
ER -

CSL-JSON

{
"id": "10.1016/j.ibneur.2026.03.002",
"type": "article-journal",
"title": "Differential quadruple pattern: A new EEG signal classification framework",
"container-title": "IBRO neuroscience reports",
"author": [
{
"family": "Ozgor",
"given": "Bilge"
},
{
"family": "Goktas",
"given": "Omer Faruk"
},
{
"family": "Baygin",
"given": "Mehmet"
},
{
"family": "Dogan",
"given": "Sengul"
},
{
"family": "Tuncer",
"given": "Turker"
}
],
"container-title-short": "IBRO Neurosci Rep",
"volume": "20",
"page": "492-505",
"DOI": "10.1016/j.ibneur.2026.03.002",
"PMID": "41907560",
"PMCID": "PMC13019922",
"ISSN": "2667-2421",
"publisher": "Elsevier",
"URL": "https://doi.org/10.1016/j.ibneur.2026.03.002",
"language": "en",
"issued": {
"date-parts": [
[
2026,
3,
5
]
]
}
}

Similar papers

The papers with a page that share the most with this one: the tools found in their code, their categories, datasets, cited references and authors, the rarest counting most.

[1] doi:10.1038/s41598-026-41821-8
Quantum inspired feature engineering for explainable EEG signal classification.
Journal: Scientific reports
In common: figshare 1, methods / tools, EEG, 3 references
[2] doi:10.1007/s40120-026-00924-0
Artificial Intelligence and Machine Learning in Pediatric Epilepsy: A Systematic Review.
Journal: Neurology and therapy
In common: EEG, 5 references
[3] doi:10.1038/s41598-026-54608-8
Predicting depth of anaesthesia from single-channel EEG using a deep TCN-BiLSTM-attention model with EWMA.
Journal: Scientific reports
In common: figshare 1, EEG
[4] doi:10.3390/s26113498
Autonomic Signature-Driven Anesthesia Depth Monitoring with Biomimetic Wearable ECG and Knowledge Graph-Augmented Deep Networks.
Journal: Sensors (Basel, Switzerland)
In common: figshare 1, EEG
[5] doi:10.1038/s41467-026-73553-8 [code]
Universal rhythmic architecture uncovers two modes of neural dynamics.
Journal: Nature communications
In common: figshare 1, EEG
[6] doi:10.1038/s41467-026-71406-y [code]
CellLoop: Identifying single-cell 3D genome chromatin loops.
Journal: Nature communications
In common: figshare 1
[7] doi:10.1186/s12911-026-03591-1 [code]
Different pattern: a new EEG-based method for mental performance detection.
Journal: BMC medical informatics and decision making
In common: methods / tools, EEG, 1 reference
[8] doi:10.1007/s10548-026-01214-6
Operational Transformer: An investigation of epilepsy detection.
Journal: Brain topography
In common: methods / tools, EEG, 1 reference
[9] doi:10.1093/bioinformatics/btag169 [code]
Bidirectional Mamba-2 boosts EEG super-resolution via regression and diffusion.
Journal: Bioinformatics (Oxford, England)
In common: methods / tools, EEG, 1 reference
[10] doi:10.1038/s41598-026-47627-y [code]
QuantumNeuroXAI: a quantum-inspired deep learning framework with explainability for brain signal analysis and neurological disorder detection.
Journal: Scientific reports
In common: methods / tools, EEG, 1 reference

Contribute

The authors of this paper can claim it, correct its record and validate its tracing map, and the maintainers of its code (its owner, or a public member of its organization) correct what it says of their repository; anyone signed in can ask for its removal. Every request goes to OSCR's own machine, which answers it; your account page follows them.

Sign in with ORCID to claim this paper as one of its authors, correct its record or validate its tracing map: when the paper's metadata lists your ORCID iD, you are recognized at once. Maintainers of its code: sign in with GitHub, then claim the repository on your account page.

Request its removal

To ask OSCR to remove this record, the copies of its authors' scripts or its tracing map, use the removal request page: signed in, you say who you are, what to remove and why, then review and confirm the request. Published rules decide every request (how).

Discussion, reproductions, activity

Discussion: questions and error reports about this paper and its code, from signed-in readers and its authors. It opens with sign-in.

Reproductions: reports from readers who ran the authors' code: what they reproduced, with which environment, commit and data. It opens with sign-in.

Activity: what happens around this paper: new versions of its record, its map's validation, discussions and reproductions. It opens with sign-in.