Interpretable AI for neural signal decoding in dementia: an EEG ensemble approach to differential diagnosis.

Overview

1. Department of Biomedical Engineering, Salim Habib University, Karachi, Pakistan
2. Department of Computer Sciences, College of Computer and Information Sciences, Princess Nourah Bint Abdulrahman University, Riyadh, Saudi Arabia
3. College of Computer Science, King Khalid University, Abha, Saudi Arabia
4. School of Computer Science and Electronic Engineering (CSEE), University of Essex, Colchester, United Kingdom

Abstract

Introduction: The differential diagnosis between Alzheimer’s disease (AD) and frontotemporal dementia (FTD) presents a significant clinical challenge due to overlapping early-stage symptom profiles. Conventional resting-state EEG provides limited sensitivity to the impaired neural plasticity and lateralized cortical degeneration frequently observed in FTD.

Methods: We developed a domain-informed heterogeneous ensemble framework incorporating dynamic neural reactivity and hemispheric asymmetry metrics from 19-channel EEG recordings acquired from 88 participants (36 AD, 23 FTD, 29 cognitively normal controls) during resting-state and photic stimulation paradigms. A neural reactivity vector (V_diff) was derived to quantify state-dependent spectral transitions. The 1,014-dimensional feature space was reduced to 200 features via recursive feature elimination, prioritizing spectral power distributions, hemispheric asymmetry indices (HAI), and stimulation-induced reactivity parameters. A weighted ensemble of Extreme Gradient Boosting (XGBoost) and Random Forest classifiers was evaluated using a subject-aware 90/10 holdout split with internal five-fold cross-validation.

Results: The optimized model achieved a multi-class segment-level accuracy of 95.63% on an independently held-out test partition of 1,281 segments, with an internal five-fold cross-validation mean of 0.9846 ± 0.003. FTD-specific precision reached 0.9907. SHAP analysis identified beta-band hemispheric asymmetry and alpha-band reactivity as the principal contributors to class separation.

Discussion: These findings indicate that the integration of dynamic state-transition measures with structural asymmetry proxies enhances electrophysiological discrimination between dementia subtypes. The framework provides a computationally efficient and biologically interpretable alternative to deep learning–based methodologies for EEG-driven dementia classification.

Reproduced under the paper's license (CC BY), from the paper cited above.

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

• openneuro:ds004504 — at OpenNeuro; found in “Data availability statement”
• openneuro:ds006036 — at OpenNeuro; found in “Data availability statement”

Data availability statement

The datasets analyzed in this study are publicly available on OpenNeuro. The resting-state EEG dataset can be accessed at https://openneuro.org/datasets/ds004504 and the photic stimulation dataset at https://openneuro.org/datasets/ds006036.

Reproduced under the paper's license (CC BY), from the paper cited above.

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 2, 28 September 2026

• Funding: added Princess Nourah Bint Abdulrahman University: PNURSP2026R232; King Khalid University

Version 1, 27 September 2026: the first record

Recorded: type, language, journal, volume, pages, dates, 6 authors, 5 keywords, 32 references.

Cite

This paper

Muhammad, F., Usmani, I. A., Aamir, M., Alduailij, M., Marzougui, M., & Nawaz, R. (2026). Interpretable AI for neural signal decoding in dementia: an EEG ensemble approach to differential diagnosis. Frontiers in neuroinformatics, 20, 1902549. https://doi.org/10.3389/fninf.2026.1902549

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/s41598-026-57069-1

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

Journal: Scientific reports

In common: OpenNeuro ds006036, OpenNeuro ds004504, Alzheimer's / dementia, EEG, clinical / translational

[2] 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, 3 references

[3] 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, 3 references

[4] 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, 1 reference

[5] doi:10.3389/fpsyt.2026.1803720 [code]

NeuroCon-AutismNet: a privacy-preserving multimodal framework toward autism screening via diffusion-regularized EEG biomarkers and empathy-aware multilingual dialogue.

Journal: Frontiers in psychiatry

In common: EEG, clinical / translational, 1 reference

[6] doi:10.1007/s00429-025-03012-5 [code]

The neurophysiology of healthy and pathological aging: a comprehensive systematic review

Journal: —

In common: Alzheimer's / dementia, EEG, clinical / translational, 1 reference

[7] doi:10.1016/j.mex.2026.104077 [code]

Structured multi-domain EEG descriptors with phase-based connectivity for lie and truth detection.

Journal: MethodsX

In common: EEG, 1 reference

[8] doi:10.1002/mds.70348 [code]

Electroencephalography-Based Clustering Reveals Robust Neurophysiological Subtypes in Parkinson's Disease.

Journal: Movement disorders : official journal of the Movement Disorder Society

In common: EEG, 1 reference

[9] doi:10.3389/fnagi.2026.1789422

Short-term neurovascular and electrophysiological responses to combined visual and vibration stimulation in older adults with mild cognitive impairment.

Journal: Frontiers in aging neuroscience

In common: Alzheimer's / dementia, EEG, 1 reference

[10] doi:10.1002/nep3.70054 [code]

Serum elemental profile-based machine learning models for Alzheimer's disease identification and cognitive score prediction.

Journal: Neuroprotection (Chichester, England)

In common: Alzheimer's / dementia, clinical / translational, 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.

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: 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.