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A novel and accurate EEG emotion classification model based on multiple attention local binary patterns.

Overview

Authors: Hakan Koksal1, Kubra Yildirim1, Jagadish Nayak2, Mehmet Baygin3, Prabal Datta Barua4, Burak Tasci5, Sengul Dogan1, Turker Tuncer1, U R Acharya6
  1. Department of Digital Forensics Engineering, Technology Faculty, Firat University, Elazig, Turkey
  2. Electrical and Electronics Engineering Department, BITS Pilani Dubai Campus, Dubai, UAE
  3. Department of Computer Engineering, College of Engineering, Erzurum Technical University, Erzurum, Turkey
  4. School of Business (Information System), University of Southern Queensland, Springfield, Australia
  5. Vocational School of Technical Sciences, Firat University, Elazig, 23119 Turkey
  6. School of Mathematics, Physics and Computing, University of Southern Queensland, Springfield, Australia
Institutions: Fırat University (Türkiye); Birla Institute of Technology and Science, Pilani - Dubai Campus (United Arab Emirates); Erzurum Technical University (Türkiye); University of Southern Queensland (Australia)
Journal: BMC medicine, volume 24, issue 1, article 419
Dates: received 23 December 2025; accepted 13 May 2026; published online 1 June 2026
Type: Research article · Language: English
License: CC BY-NC-ND
Identifiers: DOI 10.1186/s12916-026-04940-7 · PMID 42219487 · PMCID PMC13439851 · OpenAlex W7162947421
Open access: gold, a free copy (OpenAlex)
Status: data only
Categories: EEG (modality), human (organism), cognitive (subfield)
Methods: Spectral & time-frequency, Preprocessing, Smoothing, state filtering, decompositions, Machine learning, Statistics, Physiology & signal measures
Keywords: Attention LBP, EEG emotion classification, Semantic cortex map, Feature engineering, Information fusion
MeSH: Electroencephalography*, Emotions*, Classification Algorithms, Female, Humans, Machine Learning, Male, Signal Processing, Computer-Assisted (* major topic)
Topic: Emotion and Mood Recognition (Experimental and Cognitive Psychology, Psychology), according to OpenAlex
Funding: Scientific Research Projects Coordination Unit of Firat University (TBMYO.25.08)
Citations: not cited yet (Europe PMC); 71 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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The paper's code and data availability statement is in the Data section.

Tracing map

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Data

Datasets cited

Code and data availability statement

The paper has a code and data availability statement. Its license (CC BY-NC-ND) does not allow reproducing it here; in short, from what the harvester recognized in it:

  • it says that the data are available on request

Read it in the paper: doi.org/10.1186/s12916-026-04940-7.

Versions

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Version 1, 27 September 2026: the first record

Recorded: type, language, journal, volume, issue, pages, dates, 9 authors, 5 keywords, 8 MeSH terms, 1 funder, 25 references.

Cite

This paper

Koksal, H., Yildirim, K., Nayak, J., Baygin, M., Barua, P. D., Tasci, B., Dogan, S., Tuncer, T., & Acharya, U. R. (2026). A novel and accurate EEG emotion classification model based on multiple attention local binary patterns. BMC medicine, 24(1), 419. https://doi.org/10.1186/s12916-026-04940-7

BibTeX

@article{koksal2026novel,
author = {Koksal, Hakan and Yildirim, Kubra and Nayak, Jagadish and Baygin, Mehmet and Barua, Prabal Datta and Tasci, Burak and Dogan, Sengul and Tuncer, Turker and Acharya, U R},
title = {{A novel and accurate EEG emotion classification model based on multiple attention local binary patterns}},
journal = {BMC medicine},
year = {2026},
month = jun,
volume = {24},
number = {1},
pages = {419},
publisher = {BioMed Central},
issn = {1741-7015},
doi = {10.1186/s12916-026-04940-7},
url = {https://doi.org/10.1186/s12916-026-04940-7},
pmid = {42219487},
pmcid = {PMC13439851}
}

RIS

TY - JOUR
AU - Koksal, Hakan
AU - Yildirim, Kubra
AU - Nayak, Jagadish
AU - Baygin, Mehmet
AU - Barua, Prabal Datta
AU - Tasci, Burak
AU - Dogan, Sengul
AU - Tuncer, Turker
AU - Acharya, U R
TI - A novel and accurate EEG emotion classification model based on multiple attention local binary patterns
T2 - BMC medicine
J2 - BMC Med
PY - 2026
DA - 2026/06/01
VL - 24
IS - 1
SP - 419
SN - 1741-7015
PB - BioMed Central
DO - 10.1186/s12916-026-04940-7
UR - https://doi.org/10.1186/s12916-026-04940-7
LA - en
ER -

CSL-JSON

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"container-title": "BMC medicine",
"author": [
{
"family": "Koksal",
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"container-title-short": "BMC Med",
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"page": "419",
"DOI": "10.1186/s12916-026-04940-7",
"PMID": "42219487",
"PMCID": "PMC13439851",
"ISSN": "1741-7015",
"publisher": "BioMed Central",
"URL": "https://doi.org/10.1186/s12916-026-04940-7",
"language": "en",
"issued": {
"date-parts": [
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}

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