Parkinson's disease classification using optimized attention-based deep learning from EEG signals with interpretable...

Parkinson's disease (PD) diagnosis remains challenging because subtle neural alterations may be difficult to capture using conventional clinical assessment alone. This study proposes an attention-based deep learning framework for classifying PD from resting-state EEG with minimal preprocessing and leakage-safe evaluation. Raw EEG recordings were first partitioned at the subject level. Within each fold, the selected motor-related EEG channel was decomposed into canonical sub-bands using discrete wavelet transform, and the resulting sub-band signals were then segmented into overlapping temporal windows. Each sub-band window was transformed into a time-frequency spectrogram using the short-time Fourier transform and classified using a ResNet-101 backbone enhanced with dual channel-spatial attention. Hyperparameters were optimized using an Enhanced Adaptive Hybrid Covariance Matrix Adaptation Evolution Strategy (AH-CMA-ES), applied only within training/internal-validation subjects in each fold. Model performance was evaluated on two independent public EEG datasets, UC San Diego and University of Iowa, using subject-wise nested leave-one-subject-out cross-validation. In each fold, the held-out subject was excluded from training, augmentation, hyperparameter optimization, early stopping, and model selection. The proposed framework achieved 95.2% segment-level and 96.77% subject-level accuracy on UCSD, and 92.1% segment-level and 92.86% subject-level accuracy on Iowa, with subject-level decisions obtained by majority voting over non-augmented test segments. In addition to classification, sub-band topographical analysis provided exploratory neurophysiological interpretation across canonical EEG rhythms, revealing patterns consistent with reported PD-related oscillatory alterations. These findings suggest that resting-state EEG combined with attention-based deep learning can support robust, interpretable PD classification, while larger heterogeneous cohorts are needed to further validate clinical generalizability.