Benchmarking of EEG Analysis Techniques for Parkinson's Disease Diagnosis: A Comparison between Traditional ML Methods and Foundation DL Methods

TL;DR

This study systematically benchmarks traditional machine learning and deep learning models for Parkinson's Disease diagnosis using EEG data, highlighting the effectiveness of CNN-LSTM architectures and traditional classifiers like XGBoost.

Contribution

It provides a unified evaluation framework and baseline results for EEG-based PD classification, facilitating future research and comparison.

Findings

Deep learning models, especially CNN-LSTM, outperform other architectures.

Traditional classifiers like XGBoost also show strong accuracy.

Consistent preprocessing and evaluation improve model comparability.

Abstract

Parkinson's Disease PD is a progressive neurodegenerative disorder that affects motor and cognitive functions with early diagnosis being critical for effective clinical intervention Electroencephalography EEG offers a noninvasive and costeffective means of detecting PDrelated neural alterations yet the development of reliable automated diagnostic models remains a challenge In this study we conduct a systematic benchmark of traditional machine learning ML and deep learning DL models for classifying PD using a publicly available oddball task dataset Our aim is to lay the groundwork for developing an effective learning system and to determine which approach produces the best results We implement a unified sevenstep preprocessing pipeline and apply consistent subjectwise crossvalidation and evaluation criteria to ensure comparability across models Our results demonstrate that while baseline deep learning architectures particularly CNNLSTM models achieve the best performance compared to other deep learning architectures underlining the importance of capturing longrange temporal dependencies several traditional classifiers such as XGBoost also offer strong predictive accuracy and calibrated decision boundaries By rigorously comparing these baselines our work provides a solid reference framework for future studies aiming to develop and evaluate more complex or specialized architectures Establishing a reliable set of baseline results is essential to contextualize improvements introduced by novel methods ensuring scientific rigor and reproducibility in the evolving field of EEGbased neurodiagnostics