Attention-Guided Rest–Walk EEG Modeling for Parkinson’s Disease Classification Using CNN Features and Transformer Encoding

Abstract

Parkinson’s disease (PD) is associated with motor impairment and altered cortical dynamics. Although resting-state electroencephalography (EEG) has been widely studied for PD classification, walking EEG remains comparatively underexplored despite its relevance to gait dysfunction. This study developed an EEG-only, leakage-free framework for participant-level classification of PD and healthy controls (HCs) using resting- and walking-state EEG data from the OpenNeuro ds007526 dataset. Following selection, preprocessing, and quality control, 132 participants were retained, including 109 individuals with PD and 23 HCs. EEG recordings were resampled to 128 Hz, filtered between 0.5 and 40 Hz, screened for artifacts, harmonized across conditions, and segmented into overlapping 6 s windows. Classical machine-learning (ML) models used participant-level engineered features, whereas deep-learning (DL) models classified pooled resting-state and walking windows. Extra Trees achieved the highest balanced accuracy (0.71) among the ML, while ShallowConvNet-Lite was the strongest standard DL baseline (0.82). The proposed NeuroAtten-PD model achieved an accuracy of 0.86, a balanced accuracy of 0.84, an F1-score of 0.91, an ROC-AUC of 0.90, and a sensitivity of 0.88. After Holm correction, participant-level paired comparisons indicated significant differences from DeepConvNet-Lite and EEGNet-Lite. These findings demonstrate the feasibility of participant-level PD classification using a pooled collection of resting-state and walking EEG windows. With validation in larger, independent, and more balanced clinical cohorts, the proposed framework could support objective EEG-based decision support and provide a foundation for portable or wearable systems for longitudinal monitoring of PD-related cortical changes in clinical and home-based settings.