A Comparison of Machine Learning Models for Classification of Parkinson's Disease During a Working Memory and Sustained Attention Task.

Overview

1. Biomedical Engineering Department, University of North Dakota, Grand Forks, ND 58202, USA
2. Department of Psychology, North Dakota State University, Fargo, ND 58102, USA; (S.A.B.); (J.S.J.)
3. Department of Neurology, Sanford Health, Fargo, ND 58103, USA; (J.-S.L.); (J.K.)
4. Department of Neurology, University of North Dakota School of Medicine and Health Science, Grand Forks, ND 58202, USA
5. Department of Psychology, Minnesota State Community and Technical College, Moorhead, MN 56560, USA

Abstract

Highlights: What are the main findings? A task-aligned machine learning pipeline combining EEG and pupillometry features separated individuals with Parkinson’s disease from healthy controls during a working memory task, achieving accuracy that exceeded what standard behavioral performance measures alone could distinguish between the two groups. Seven physiological features, including markers of frontal alpha activity, oscillatory ratios, and pupillary response, emerged as most discriminative, concentrated in the most demanding parts of the task (higher memory loads, longer delays, and incorrect trials).

What are the implications of the main findings? Physiological signals recorded during cognitive tasks may capture Parkinson’s-related cognitive changes that are not reliably evident from behavioral performance alone, pointing to their potential value as more sensitive early markers of cognitive dysfunction. The task-aligned, interpretable design of this framework offers a reproducible platform for future biomarker discovery, laying groundwork for larger validation studies and, eventually, tools to support cognitive screening and monitoring in Parkinson’s disease.

Abstract: Background and Objectives: Individuals with Parkinson’s disease (PD) experience deficits in working memory (WM) and sustained attention (ATTN), but diagnosing and monitoring these deficits remains challenging. This study compares machine learning (ML) classification models trained on EEG and pupillometry data from WM and ATTN tasks to identify task-specific and shared cognitive biomarkers of PD. Methods: EEG and pupillometry were recorded from PD patients and healthy controls (HC) during a visual change detection WM task and a continuous performance ATTN task. A standardized toolbox extracted 108 features, reduced via PCA and recursive elimination (RE) and classified using an SVM-RBF within a nested, 5-fold cross-validated pipeline, with class balancing (SMOTE) and feature selection performed strictly within training folds to prevent leakage. Results: On internal test folds, WM achieved 71% accuracy (F1 = 0.701) and ATTN achieved 73% (F1 = 0.699); on an independent hold-out set, WM achieved 63% accuracy (F1 = 0.626) and ATTN achieved 70% (F1 = 0.623). ATTN showed higher accuracy and precision, WM showed higher recall, and univariate analyses independently supported several top-ranked features (e.g., theta/beta and alpha/theta ratios); PAI and FAA, though top features in both tasks, reached univariate significance only in ATTN. These results indicate WM and ATTN yield complementary, task-linked neurophysiological signatures relevant to PD classification. Conclusion: At its current stage, this pipeline functions as a research tool for biomarker discovery rather than a clinical diagnostic, though larger, externally validated samples could support future screening and monitoring applications.

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

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Data

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Data Availability Statement

The de-identified EEG and pupillometry datasets generated and analyzed during the current study are available from the corresponding author upon reasonable request. Access to de-identified raw data from human participants can be available to Jeffrey S. Johnson or Samuel A. Birkholz upon reasonable request. The custom feature extraction toolbox and scripts used for model training and analysis are openly available on GitHub at [https://github.com/mercedesterry/EEG-and-Pupillary-Feature-Extraction-Toolbox] (accessed on 20 July 2025).

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

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Version 1, 27 September 2026: the first record

Recorded: type, language, journal, volume, issue, pages, dates, 7 authors, 10 keywords, 1 funder, 42 references.

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This paper

Terry, M. A., Birkholz, S. A., Johnson, J. S., Lou, J.-S., Huether, A. X. A., Keller, J., & Alvarez-Vazquez, E. (2026). A Comparison of Machine Learning Models for Classification of Parkinson's Disease During a Working Memory and Sustained Attention Task. Brain sciences, 16(8), 781. https://doi.org/10.3390/brainsci16080781

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