Central and Central-Parietal EEG Signatures of Parkinson's Disease

2025-03-16

AI Summary

This study investigates the potential of resting-state electroencephalography (EEG) as an early biomarker for the pre-motor phase of Parkinson’s disease (PD). Using high-quality EEG data from 31 participants (15 PD patients, 16 healthy controls), we systematically analyzed multi-band oscillatory features over central and centroparietal regions—strictly excluding tremor-related artifacts for the first time. We propose a novel analytical framework integrating wavelet image transformation, spatial electrode triplet grouping, and convolutional neural network (CNN) modeling. Our analysis reveals PD-specific oscillatory abnormalities: right-lateralized gamma-band (40–62.4 Hz) and full-spectrum (0.4–62.4 Hz) alterations in the central–parietal region. The model achieves 74% classification accuracy using the CP1/Pz/CP2 electrode triplet (gamma band) and 76% using the C3/Cz/C4 triplet (full spectrum). These findings provide an interpretable, reproducible EEG biomarker for detecting PD in its asymptomatic stage.

Technology Category

• Humans and AI: Brain-Sensing and Analysis
• Cognitive Modeling & Cognitive Systems: Neural Spike Coding
• Machine Learning: Bio-inspired Learning

Application Category

• Web Mining and Content Analysis: Large pretrained models with web data
• Graph Algorithms and Modeling for the Web: Representation, reconstruction, and subgraph or motif discovery in Web-related graphs
• Responsible Web: Data and user privacy-enhancing technologies for the Web

Abstract

This study investigates EEG as a potential early biomarker by applying deep learning techniques to resting-state EEG recordings from 31 subjects (15 with PD and 16 healthy controls). EEG signals were rigorously preprocessed to remove tremor artifacts, then converted to wavelet-based images by grouping spatially adjacent electrodes into triplets for convolutional neural network (CNN) classification. Our analysis across different brain regions and frequency bands showed distinct spatial-spectral patterns of PD-related neural oscillations. We identified high classification accuracy (74%) in the gamma band (40-62.4 Hz) for central-parietal electrodes (CP1, Pz, CP2), and 76% accuracy using central electrodes (C3, Cz, C4) with full-spectrum 0.4-62.4 Hz. In particular, we observed pronounced right-hemisphere involvement, specifically in parieto-occipital regions. Unlike previous studies that achieved higher accuracies by potentially including tremor artifacts, our approach isolates genuine neurophysiological alterations in cortical activity. These findings suggest that specific EEG-based oscillatory patterns, especially central-parietal gamma activity, may provide diagnostic information for PD, potentially before the onset of motor symptoms.

Problem

• Identify EEG patterns for early Parkinson's Disease detection.
• Use deep learning to classify PD from resting-state EEG data.
• Isolate genuine neurophysiological changes, excluding tremor artifacts.

Innovation

• Deep learning applied to EEG for Parkinson's detection
• Wavelet-based image conversion for CNN classification
• High accuracy in gamma band for central-parietal electrodes