Lightweight Spatial–Frequency Fusion Framework for EEG-Based Parkinson’s Disease Detection

Reliable and efficient methods to identify Parkinson’s disease (PD) from EEG signals are highly valuable for studying early disease markers and supporting timely intervention research. In this work, we propose a lightweight deep learning framework for PD detection using electroencephalogram (EEG) signals. The framework employs a novel spatial–frequency multiscale feature fusion strategy to characterize both local neural activity and global inter-channel connectivity. To achieve high accuracy with low computational cost, we design a hybrid network combining the DCFConv lightweight convolution module and the OSGAttn orthogonal global attention module. Experiments on the public Iowa PD-EEG dataset show that our method achieves 99.81% accuracy, 100% precision, and 99.62% sensitivity, while maintaining only 0.16 M parameters and 44.08 M FLOPs. The results demonstrate that the proposed approach provides an effective, efficient, and reliable solution for PD EEG signal classification.