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NeuroNetFusion: enhanced EEG abnormality classification via multi-network TF-IDF feature selection.

Overview

Authors: Sangjin Ahn1, So Yeon Kim1,2, Kyung-Ah Sohn1,2
  1. Department of Artificial Intelligence, Ajou University,Suwon, 16499 Korea
  2. Department of Software and Computer Engineering, Ajou University,Suwon, 16499 Korea
Institutions: Ajou University (South Korea)
Journal: Scientific reports, volume 16, issue 1, article 21781
Dates: received 3 November 2025; accepted 7 May 2026; published online 12 May 2026
Type: Research article · Language: English
License: CC BY-NC-ND
Identifiers: DOI 10.1038/s41598-026-52750-x · PMID 42120673 · PMCID PMC13358131 · OpenAlex W7160963218
Open access: gold, a free copy (OpenAlex)
Status: data only
Categories: EEG (modality), human (organism)
Methods: Statistics, Machine learning, Connectivity, Smoothing, state filtering, decompositions
Keywords: EEG classification, Network fusion, Feature selection, Genetic algorithm, Computational biology and bioinformatics, Engineering, Mathematics and computing, Neuroscience
MeSH: Electroencephalography*, Signal Processing, Computer-Assisted*, Algorithms, Humans, Wavelet Analysis (* major topic)
Topic: EEG and Brain-Computer Interfaces (Cognitive Neuroscience, Neuroscience), according to OpenAlex
Funding: Institute for Information & Communications Technology Planning & Evaluation (IITP) funded by MSIT (IITP-2026-RS-2023-00255968); National Research Foundation of Korea (NRF) funded by MSIT (RS-2026-25469593)
Citations: not cited yet (Europe PMC); 53 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/s41598-026-52750-x.

Versions

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Version 1, 28 September 2026: the first record

Recorded: type, language, journal, volume, issue, pages, dates, 3 authors, 8 keywords, 5 MeSH terms, 2 funders, 39 references.

Cite

This paper

Ahn, S., Kim, S. Y., & Sohn, K.-A. (2026). NeuroNetFusion: enhanced EEG abnormality classification via multi-network TF-IDF feature selection. Scientific reports, 16(1), 21781. https://doi.org/10.1038/s41598-026-52750-x

BibTeX

@article{ahn2026neuronetfusion,
author = {Ahn, Sangjin and Kim, So Yeon and Sohn, Kyung-Ah},
title = {{NeuroNetFusion: enhanced EEG abnormality classification via multi-network TF-IDF feature selection}},
journal = {Scientific reports},
year = {2026},
month = may,
volume = {16},
number = {1},
pages = {21781},
publisher = {Nature Publishing Group},
issn = {2045-2322},
doi = {10.1038/s41598-026-52750-x},
url = {https://doi.org/10.1038/s41598-026-52750-x},
pmid = {42120673},
pmcid = {PMC13358131}
}

RIS

TY - JOUR
AU - Ahn, Sangjin
AU - Kim, So Yeon
AU - Sohn, Kyung-Ah
TI - NeuroNetFusion: enhanced EEG abnormality classification via multi-network TF-IDF feature selection
T2 - Scientific reports
J2 - Sci Rep
PY - 2026
DA - 2026/05/12
VL - 16
IS - 1
SP - 21781
SN - 2045-2322
PB - Nature Publishing Group
DO - 10.1038/s41598-026-52750-x
UR - https://doi.org/10.1038/s41598-026-52750-x
LA - en
ER -

CSL-JSON

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"language": "en",
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