Quantum inspired feature engineering for explainable EEG signal classification.
Overview
- College of Applied Computer Sciences (CACS), King Saud University, Riyadh, 11543 Saudi Arabia
- Department of Computer Engineering, College of Engineering, Erzurum Technical University, Erzurum, Turkey
- Department of Electronics and Automation, Technical Sciences Vocational School, Ankara Yildirim Beyazit University, Ankara, Turkey
- Department of Digital Forensics Engineering, College of Technology, Firat University, 23119 Elazig, Turkey
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.
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Data
Datasets cited
- figshare:1, at figshare; found in the references
Data availability statement
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Read it in the paper: doi.org/10.1038/s41598-026-41821-8.
Versions
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Version 1, 30 September 2026: the first record
Recorded: type, language, journal, volume, issue, pages, dates, 8 authors, 9 keywords, 7 MeSH terms, 1 funder, 36 references.
Cite
This paper
Alotaibi, F. A., Yagmahan, M. S. N., Alobaid, K. A., Jari, M., Goktas, O. F., Baygin, M., Dogan, S., & Tuncer, T. (2026). Quantum inspired feature engineering for explainable EEG signal classification. Scientific reports, 16(1), 12424. https://
BibTeX
@article{alotaibi2026qua
author = {Alotaibi, Fahad A and Yagmahan, Mehmet Said Nur and Alobaid, Khalid A and Jari, Mousa and Goktas, Omer Faruk and Baygin, Mehmet and Dogan, Sengul and Tuncer, Turker},
title = {{Quantum inspired feature engineering for explainable EEG signal classification}},
journal = {Scientific reports},
year = {2026},
month = mar,
volume = {16},
number = {1},
pages = {12424},
publisher = {Nature Publishing Group},
issn = {2045-2322},
doi = {10.1038/
url = {https://
pmid = {41792299},
pmcid = {PMC13083839}
}
RIS
TY - JOUR
AU - Alotaibi, Fahad A
AU - Yagmahan, Mehmet Said Nur
AU - Alobaid, Khalid A
AU - Jari, Mousa
AU - Goktas, Omer Faruk
AU - Baygin, Mehmet
AU - Dogan, Sengul
AU - Tuncer, Turker
TI - Quantum inspired feature engineering for explainable EEG signal classification
T2 - Scientific reports
J2 - Sci Rep
PY - 2026
DA - 2026/
VL - 16
IS - 1
SP - 12424
SN - 2045-2322
PB - Nature Publishing Group
DO - 10.1038/
UR - https://
LA - en
ER -
CSL-JSON
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