OSCR

Explainable EEG-based machine learning for early diagnosis of Alzheimer's disease and frontotemporal dementia.

Overview

Authors: Fahman Saeed1, Sultan Aldera1
  1. Computer Science Department, College of Computer and Information Sciences, Imam Mohammad Ibn Saud Islamic University (IMSIU),Riyadh, Saudi Arabia
Institutions: Imam Mohammad ibn Saud Islamic University (Saudi Arabia)
Journal: Scientific reports, volume 16, issue 1, article 19775
Dates: received 1 October 2025; accepted 4 June 2026; published online 16 June 2026
Type: Research article · Language: English
License: CC BY-NC-ND
Identifiers: DOI 10.1038/s41598-026-57069-1 · PMID 42297902 · PMCID PMC13315799 · OpenAlex W7164809499
Open access: gold, a free copy (OpenAlex)
Status: data only
Categories: EEG (modality), human (organism), Alzheimer's / dementia (population), clinical / translational (subfield)
Methods: Statistics, Smoothing, state filtering, decompositions, Machine learning, Physiology & signal measures
Keywords: EEG-based dementia diagnosis, Independent component analysis (ICA), AutoML, State Space Model (SSM), Dementia subtypes classification, Cognitive severity regression, Neurodegenerative disease biomarkers, Computational biology and bioinformatics, Diseases, Neurology, Neuroscience
MeSH: Alzheimer Disease*, Electroencephalography*, Frontotemporal Dementia*, Machine Learning*, Aged, Early Diagnosis, Female, Humans (* major topic)
Topic: EEG and Brain-Computer Interfaces (Cognitive Neuroscience, Neuroscience), according to OpenAlex
Funding: King Salman Center for Disability Research (KSRG-2024-467); Deanship of Scientific Research at Imam Mohammad Ibn Saud Islamic University (IMSIU) (IMSIU-DDRSP2601)
Citations: not cited yet (Europe PMC); 30 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.

The paper's code and data availability statement is in 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

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:

Read it in the paper: doi.org/10.1038/s41598-026-57069-1.

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, 27 September 2026: the first record

Recorded: type, language, journal, volume, issue, pages, dates, 2 authors, 11 keywords, 8 MeSH terms, 2 funders, 24 references.

Cite

This paper

Saeed, F., & Aldera, S. (2026). Explainable EEG-based machine learning for early diagnosis of Alzheimer's disease and frontotemporal dementia. Scientific reports, 16(1), 19775. https://doi.org/10.1038/s41598-026-57069-1

BibTeX

@article{saeed2026explainable,
author = {Saeed, Fahman and Aldera, Sultan},
title = {{Explainable EEG-based machine learning for early diagnosis of Alzheimer's disease and frontotemporal dementia}},
journal = {Scientific reports},
year = {2026},
month = jun,
volume = {16},
number = {1},
pages = {19775},
publisher = {Nature Publishing Group},
issn = {2045-2322},
doi = {10.1038/s41598-026-57069-1},
url = {https://doi.org/10.1038/s41598-026-57069-1},
pmid = {42297902},
pmcid = {PMC13315799}
}

RIS

TY - JOUR
AU - Saeed, Fahman
AU - Aldera, Sultan
TI - Explainable EEG-based machine learning for early diagnosis of Alzheimer's disease and frontotemporal dementia
T2 - Scientific reports
J2 - Sci Rep
PY - 2026
DA - 2026/06/16
VL - 16
IS - 1
SP - 19775
SN - 2045-2322
PB - Nature Publishing Group
DO - 10.1038/s41598-026-57069-1
UR - https://doi.org/10.1038/s41598-026-57069-1
LA - en
ER -

CSL-JSON

{
"id": "10.1038/s41598-026-57069-1",
"type": "article-journal",
"title": "Explainable EEG-based machine learning for early diagnosis of Alzheimer's disease and frontotemporal dementia",
"container-title": "Scientific reports",
"author": [
{
"family": "Saeed",
"given": "Fahman"
},
{
"family": "Aldera",
"given": "Sultan"
}
],
"container-title-short": "Sci Rep",
"volume": "16",
"issue": "1",
"page": "19775",
"DOI": "10.1038/s41598-026-57069-1",
"PMID": "42297902",
"PMCID": "PMC13315799",
"ISSN": "2045-2322",
"publisher": "Nature Publishing Group",
"URL": "https://doi.org/10.1038/s41598-026-57069-1",
"language": "en",
"issued": {
"date-parts": [
[
2026,
6,
16
]
]
}
}

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/s41598-026-43431-w
A deep-SVM hybrid framework with enhanced EEG feature engineering and SHAP-based explainability for Alzheimer's classification.
Journal: Scientific reports
In common: OSF 2v5md, OpenNeuro ds006036, 1 other dataset, Alzheimer's / dementia, EEG, 1 reference
[2] doi:10.3390/diagnostics16050746
Deep Learning-Based Alzheimer's Disease Detection from Multi-Channel EEG Using Fused Time-Frequency Image Grids.
Journal: Diagnostics (Basel, Switzerland)
In common: OpenNeuro ds006036, Alzheimer's / dementia, EEG, 4 references
[3] doi:10.3389/fninf.2026.1902549
Interpretable AI for neural signal decoding in dementia: an EEG ensemble approach to differential diagnosis.
Journal: Frontiers in neuroinformatics
In common: OpenNeuro ds006036, OpenNeuro ds004504, Alzheimer's / dementia, EEG, clinical / translational
[4] doi:10.3389/fnins.2026.1871265
Clustered pattern projection for EEG dementia classification: evaluating the reliability of disorder patterns.
Journal: Frontiers in neuroscience
In common: OpenNeuro ds004504, Alzheimer's / dementia, EEG, 2 references
[5] doi:10.1038/s41598-026-42452-9
Quantitative EEG signatures of power and functional connectivity alterations in Alzheimer's disease and frontotemporal dementia.
Journal: Scientific reports
In common: OpenNeuro ds004504, Alzheimer's / dementia, EEG, clinical / translational, 1 reference
[6] doi:10.1007/s11571-026-10464-w
The AHEPA EEG benchmark: setting the standard for machine learning in dementia diagnosis, a scoping review.
Journal: Cognitive neurodynamics
In common: Alzheimer's / dementia, EEG, 4 references
[7] doi:10.1007/s10548-026-01196-5
A Quantitative Comparison of Two Methods for Higher-Order EEG Microstate Syntax Analysis.
Journal: Brain topography
In common: OpenNeuro ds004504, Alzheimer's / dementia, EEG, 1 reference
[8] doi:10.3390/brainsci16080856 [code]
Alzheimer's Disease Detection Using Combined EEG Source Connectivity and Microstate Features.
Journal: Brain sciences
In common: Alzheimer's / dementia, EEG, 3 references
[9] doi:10.1038/s41598-026-41745-3
Integrating attractor dynamics and connectivity features for EEG-based dementia classification.
Journal: Scientific reports
In common: Alzheimer's / dementia, EEG, 3 references
[10] doi:10.1186/s12859-026-06410-6
Design of a configurable SoC for Alzheimer's disease detection based on multimodal signals.
Journal: BMC bioinformatics
In common: OpenNeuro ds004504, Alzheimer's / dementia, EEG

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.