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<p>Parkinson’s disease (PD) is a progressive neurodegenerative disorder that severely affects motor and cognitive functions, making early and accurate diagnosis crucial for effective clinical management. This research introduces the high-frequency substantia nigra and ventral tegmental area fusion network (HF-SNVTA-FusionNet), a robust EEG-based PD detection framework. The system employs independent component analysis (ICA) for artifact removal, multi-domain feature extraction (time, frequency, and time–frequency), and principal component analysis (PCA) for dimensionality reduction, followed by a CNN-BiGRU-multi-head self-attention (MHSA) classification pipeline. Specifically, CNN captures local spatial patterns, BiGRU models bidirectional temporal dependencies, and MHSA refines salient features, enabling improved discrimination between PD and healthy EEG signals. Experiments on three benchmark datasets (UI, PDG, and USDRS) demonstrate superior performance, achieving accuracies of 100%, 98.89%, and 98.15% within the 50–70 Hz band using 64 PCA features. Comparative evaluation confirms its advantage over existing state-of-the-art models. The proposed system holds strong potential for real-world PD screening and can be extended to cognitive task-based EEG analysis and low-power hardware deployment.</p>

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HF-SNVTA-FusionNet: high-frequency multi-domain EEG feature fusion from the substantia nigra and ventral tegmental area for Parkinson’s disease classification

  • 期刊论文

  • 发表日期：

    2026年6月26日

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  • Kaushal Kumar,
  • Abhaya

摘要

Parkinson’s disease (PD) is a progressive neurodegenerative disorder that severely affects motor and cognitive functions, making early and accurate diagnosis crucial for effective clinical management. This research introduces the high-frequency substantia nigra and ventral tegmental area fusion network (HF-SNVTA-FusionNet), a robust EEG-based PD detection framework. The system employs independent component analysis (ICA) for artifact removal, multi-domain feature extraction (time, frequency, and time–frequency), and principal component analysis (PCA) for dimensionality reduction, followed by a CNN-BiGRU-multi-head self-attention (MHSA) classification pipeline. Specifically, CNN captures local spatial patterns, BiGRU models bidirectional temporal dependencies, and MHSA refines salient features, enabling improved discrimination between PD and healthy EEG signals. Experiments on three benchmark datasets (UI, PDG, and USDRS) demonstrate superior performance, achieving accuracies of 100%, 98.89%, and 98.15% within the 50–70 Hz band using 64 PCA features. Comparative evaluation confirms its advantage over existing state-of-the-art models. The proposed system holds strong potential for real-world PD screening and can be extended to cognitive task-based EEG analysis and low-power hardware deployment.