Channel Selected Stratified Nested Cross Validation for Clinically Relevant EEG Based Parkinsons Disease Detection

TL;DR

This paper introduces a robust nested cross-validation framework with channel selection for EEG-based Parkinson's detection, achieving high accuracy and addressing methodological flaws like data leakage for reliable clinical application.

Contribution

It presents a unified, bias-free evaluation method incorporating patient stratification, multi-layered windowing, and channel selection for improved EEG-based Parkinson's detection.

Findings

Achieved 80.6% accuracy on independent datasets.

Demonstrated state-of-the-art performance with rigorous validation.

Validated the importance of nested cross-validation for unbiased results.

Abstract

The early detection of Parkinsons disease remains a critical challenge in clinical neuroscience, with electroencephalography offering a noninvasive and scalable pathway toward population level screening. While machine learning has shown promise in this domain, many reported results suffer from methodological flaws, most notably patient level data leakage, inflating performance estimates and limiting clinical translation. To address these modeling pitfalls, we propose a unified evaluation framework grounded in nested cross validation and incorporating three complementary safeguards: (i) patient level stratification to eliminate subject overlap and ensure unbiased generalization, (ii) multi layered windowing to harmonize heterogeneous EEG recordings while preserving temporal dynamics, and (iii) inner loop channel selection to enable principled feature reduction without information leakage. Applied across three independent datasets with a heterogeneous number of channels, a convolutional neural network trained under this framework achieved 80.6% accuracy and demonstrated state of the art performance under held out population block testing, comparable to other methods in the literature. This performance underscores the necessity of nested cross validation as a safeguard against bias and as a principled means of selecting the most relevant information for patient level decisions, providing a reproducible foundation that can extend to other biomedical signal analysis domains.