Oscillatory Signatures of Parkinson's Disease: Central and Parietal EEG Alterations Across Multiple Frequency Bands
This work addresses the need for early diagnostic tools for Parkinson's Disease patients, but it is incremental as it builds on existing EEG and deep learning methods with a focus on artifact removal.
This study tackled the problem of identifying early biomarkers for Parkinson's Disease by analyzing EEG signals, achieving classification accuracies up to 76% using deep learning on specific brain regions and frequency bands.
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 underwent preprocessing to remove tremor artifacts before classification with CNNs using wavelet-based electrode triplet images. Our analysis across different brain regions and frequency bands showed distinct spatial-spectral patterns of PD-related neural oscillations. We identified high classification accuracy (76%) using central electrodes (C3, Cz, C4) with full-spectrum 0.4-62.4 Hz analysis and 74% accuracy in right parietal regions (P8, CP6, P4) with 10-second windows. Bilateral centro-parietal regions showed strong performance (67%) in the theta band (4.0-7.79 Hz), while multiple areas demonstrated some sensitivity (65%) in the alpha band (7.8-15.59 Hz). We also observed a distinctive topographical pattern of gamma band (40-62.4 Hz) alterations specifically localized to central-parietal regions, which remained consistent across different temporal windows. In particular, we observed pronounced right-hemisphere involvement across several frequency bands. 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 in central and parietal regions and across multiple frequency bands, may provide diagnostic information for PD, potentially before the onset of motor symptoms.