EEG Sensor-Based Parkinson's Disease Detection Using a Multi-Domain Feature Fusion Network

Parkinson's disease (PD) is a common neurodegenerative disorder, and accurate identification of PD is critical for clinical diagnosis and disease management. Electroencephalography (EEG) sensors provide reliable real-time brain signal acquisition, making them practical biosensing modalities for PD detection. However, due to their non-stationarity, single time-domain or frequency-domain analysis methods are insufficient to extract robust discriminative features from EEG signals. To address this challenge, we propose a multi-domain feature fusion EEG classification model, termed Multi-Domain Fusion Network (MDF-Net), which jointly integrates temporal, frequency-domain, and wavelet-domain representations for accurate PD recognition. MDF-Net employs a Temporal Attention-enhanced Temporal Convolutional Network (TTCN) to capture temporal dependencies and incorporates an improved 1D Convolutional Neural Network mixer module (Cmix) for multi-channel feature fusion. We constructed an EEG dataset of 415 subjects (289 healthy controls and 126 PD patients). Under 5-CV, the proposed method achieved a classification accuracy of 92.3%, an F1-score of 87.3%, and an AUC of 0.943. Experimental results demonstrate that multi-domain feature fusion effectively improves PD detection performance, and EEG sensor-based analysis shows strong potential for clinical application. This study provides a methodological reference for developing objective, practical computer-aided diagnostic tools for PD.