Journal of neural engineering · 2026
A hierarchical graph learning framework for VR-based motor imagery decoding driven by MVMD and feature optimization.
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Du Kaiyue, Chang Wenwen, Kong Weixuan, Huang He, Yan Guanghui, Sadiq Muhammad Tariq
Аннотация
OBJECTIVE: While virtual reality (VR) combined with action observation (AO) provides enriched visual guidance for motor imagery (MI), the decoding stability of MI Brain-Computer Interface (BCI) remains a challenge due to the inherent non-stationarity and low signal-to-noise ratio of Electroencephalography (EEG) signals in dynamic environments. Enhancing decoding accuracy and understanding neural representations in VR-based AO+MI tasks remain important challenges in neural engineering.
APPROACH: This study proposes a hierarchical graph learning framework tailored for VR-based MI decoding, leveraging multivariate variational mode decomposition (MVMD) to decouple complex EEG signals into rhythmic components for enhanced multi-domain feature analysis, hybrid feature selection for compact representation, and functional brain network modeling based on phase locking value. Each EEG trial is represented as a sparsified graph, and a hierarchical graph convolutional network is designed to capture multi-scale spatial dependencies and high-order interactions for classification.
MAIN RESULTS: Experimental evaluations on a lab-acquired VR-MI dataset demonstrate that the proposed framework achieves competitive three-class decoding accuracy. This performance surpasses baseline models such as EEGNet, as observed under VR-based motor imagery conditions. In addition to enhanced decoding performance, the study enables neurophysiological interpretation by characterizing VR-induced modulations in brain network organization.
SIGNIFICANCE: VR-based AO+MI paradigm is found to enhance sensorimotor connectivity and promote large-scale network integration, indicating more coordinated neural dynamics during MI tasks. These findings suggest that the proposed framework effectively improves VR-based AO+MI decoding while providing interpretable insights into neural mechanisms, supporting its potential for advanced BCI applications.
APPROACH: This study proposes a hierarchical graph learning framework tailored for VR-based MI decoding, leveraging multivariate variational mode decomposition (MVMD) to decouple complex EEG signals into rhythmic components for enhanced multi-domain feature analysis, hybrid feature selection for compact representation, and functional brain network modeling based on phase locking value. Each EEG trial is represented as a sparsified graph, and a hierarchical graph convolutional network is designed to capture multi-scale spatial dependencies and high-order interactions for classification.
MAIN RESULTS: Experimental evaluations on a lab-acquired VR-MI dataset demonstrate that the proposed framework achieves competitive three-class decoding accuracy. This performance surpasses baseline models such as EEGNet, as observed under VR-based motor imagery conditions. In addition to enhanced decoding performance, the study enables neurophysiological interpretation by characterizing VR-induced modulations in brain network organization.
SIGNIFICANCE: VR-based AO+MI paradigm is found to enhance sensorimotor connectivity and promote large-scale network integration, indicating more coordinated neural dynamics during MI tasks. These findings suggest that the proposed framework effectively improves VR-based AO+MI decoding while providing interpretable insights into neural mechanisms, supporting its potential for advanced BCI applications.