An Interpretable EEG Machine Learning Prototype for Alzheimers Disease Classification

Abstract

Resting-state electroencephalography (EEG) can capture the slowing of neural oscillations associated with Alzheimers disease (AD), but many machine-learning studies remain difficult to inspect, reproduce, or test. This study developed an interpretable, subject-level AD versus healthy-control classifier from the dataset OpenNeuro ds004504 and deployed it as a public research prototype. Preprocessed eyes-closed EEG recordings from 36 people with AD and 29 healthy controls were analysed. Welch power spectral density estimates were used to generate absolute and relative bandpower summaries, theta/alpha and delta/alpha ratios, a slow/fast ratio, and signal-level descriptors. Five-fold stratified cross-validation compared two Random Forest configurations, EEG Logistic Regression, an age-only baseline, and an EEG-plus-age model. EEG Logistic Regression gave the strongest mean fold-wise performance: accuracy 0.846, balanced accuracy 0.848, F1 score 0.849, and ROC-AUC 0.948. Age alone was near chance, while adding age did not improve the EEG-only model. The feature pattern was consistent with AD-related EEG slowing, including higher slow-wave-related ratios and lower relative alpha power. The trained pipeline was deployed as a Gradio application on Hugging Face Spaces. The result is a reproducible research and educational prototype, not a clinical diagnostic device, and requires external validation before any clinical interpretation.

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BibTeX RIS

BibTeX

@misc{a2026interpretable,
  title = {{An Interpretable EEG Machine Learning Prototype for Alzheimers Disease Classification}},
  author = {A. DAS and A. Chakravarty},
  howpublished = {bioRxiv},
  year = {2026},
  month = sep,
  doi = {10.64898/2026.09.12.751104},
  url = {https://www.biorxiv.org/content/10.64898/2026.09.12.751104v1},
  note = {CC BY-ND},
}

RIS

TY  - UNPB
TI  - An Interpretable EEG Machine Learning Prototype for Alzheimers Disease Classification
AU  - A. DAS
AU  - A. Chakravarty
PY  - 2026
DA  - 2026/09/12
PB  - bioRxiv
DO  - 10.64898/2026.09.12.751104
UR  - https://www.biorxiv.org/content/10.64898/2026.09.12.751104v1
AB  - Resting-state electroencephalography (EEG) can capture the slowing of neural oscillations associated with Alzheimers disease (AD), but many machine-learning studies remain difficult to inspect, reproduce, or test. This study developed an interpretable, subject-level AD versus healthy-control classifier from the dataset OpenNeuro ds004504 and deployed it as a public research prototype. Preprocessed eyes-closed EEG recordings from 36 people with AD and 29 healthy controls were analysed. Welch power spectral density estimates were used to generate absolute and relative bandpower summaries, theta/alpha and delta/alpha ratios, a slow/fast ratio, and signal-level descriptors. Five-fold stratified cross-validation compared two Random Forest configurations, EEG Logistic Regression, an age-only baseline, and an EEG-plus-age model. EEG Logistic Regression gave the strongest mean fold-wise performance: accuracy 0.846, balanced accuracy 0.848, F1 score 0.849, and ROC-AUC 0.948. Age alone was near chance, while adding age did not improve the EEG-only model. The feature pattern was consistent with AD-related EEG slowing, including higher slow-wave-related ratios and lower relative alpha power. The trained pipeline was deployed as a Gradio application on Hugging Face Spaces. The result is a reproducible research and educational prototype, not a clinical diagnostic device, and requires external validation before any clinical interpretation.
ER  -

Abstract

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