Parkinson's Disease Classification via EEG: All You Need is a Single Convolutional Layer

TL;DR

This paper tackles EEG-based Parkinson's disease classification by proposing LightCNN, a minimalist CNN with a single 1D convolutional layer designed for efficiency and interpretability in resting-state EEG. Despite its simplicity, LightCNN outperforms several state-of-the-art deep architectures, including CNN+GRU baselines, across key metrics and identifies neurophysiological PD biomarkers through frequency-domain analysis. The work demonstrates that a compact, well-designed model can achieve superior performance while remaining suitable for mobile or embedded deployment, and it provides insights into how frequency components drive classification. Overall, LightCNN advances practical, accurate EEG-based PD detection with potential impact on real-time clinical screening and edge-computing applications.

Abstract

In this work, we introduce LightCNN, a minimalist Convolutional Neural Network (CNN) architecture designed for Parkinson's disease (PD) classification using EEG data. LightCNN's strength lies in its simplicity, utilizing just a single convolutional layer. Embracing Leonardo da Vinci's principle that "simplicity is the ultimate sophistication," LightCNN demonstrates that complexity is not required to achieve outstanding results. We benchmarked LightCNN against several state-of-the-art deep learning models known for their effectiveness in EEG-based PD classification. Remarkably, LightCNN outperformed all these complex architectures, with a 2.3% improvement in recall, a 4.6% increase in precision, a 0.1% edge in AUC, a 4% boost in F1-score, and a 3.3% higher accuracy compared to the closest competitor. Furthermore, LightCNN identifies known pathological brain rhythms associated with PD and effectively captures clinically relevant neurophysiological changes in EEG. Its simplicity and interpretability make it ideal for deployment in resource-constrained environments, such as mobile or embedded systems for EEG analysis. In conclusion, LightCNN represents a significant step forward in efficient EEG-based PD classification, demonstrating that a well-designed, lightweight model can achieve superior performance over more complex architectures. This work underscores the potential for minimalist models to meet the needs of modern healthcare applications, particularly where resources are limited.

Figures (5)

• Figure 1: Overview of LightCNN model architecture.
• Figure 2: Feature interpretation of LightCNN: Left panel shows power spectrum (PSD) comparison between PD and healthy controls (mean$\pm$ SEM) highlighting the PD-related changes in the frequency domain. Data from all channels were averaged before PSD calculation and 5s epochs were utilized. Right panel shows the frequency response of the pooling output layer of a trained LightCNN model where x-axis is frequency (Hz), y-axis represents pooling layer output or features and the colors show average activation value.
• Figure 3: Frequency response of the convolutional layer output channels: Each pallet shows the filtering profile of a single convolutional output channel where x-axis is frequency in Hz and y-axis is power in log scale. All 59 output channels are shown from a trained LightCNN model.
• Figure 4: Ablation study: Evaluating LightCNN's performance while varying the kernel size (left) and the output channel size (right) of the convolutional layer. All performance metrics were normalized to the range 0 to 1. Performance measured on the test dataset.
• Figure 5: Example synthetic data for measuring pooling layer sensitivity (left) for 5 Hz frequency and for evaluating filtering responses of the convolutional layer output channels (right). x-axis is time in seconds and y-axis shows 59 channels.