Integrating Virtual Reality and Multifeature Fusion Deep Learning to Improve EEG-Based Motion Imagery Decoding

Objective: This study focuses on enhancing the applicability of brain–computer interface (BCI) systems for spinal cord injury (SCI) patients through improvements in electroencephalography (EEG) signal acquisition and decoding performance. Methods: This study developed an EEG acquisition optimization scheme leveraging virtual reality (VR) technology and integrated it with a deep learning framework for multi-feature fusion to improve decoding performance. To enhance immersion, VR motor imagery task scenarios (e.g., fruit picking) were designed, reducing interference from traditional screen cues and optimizing the quality of EEG signal acquisition at its source. The framework uses a continuous wavelet transform algorithm to extract time-frequency features and incorporates a hybrid architecture combining Convolutional Neural Networks and Transformer models (C-CTNet) to enable efficient multi-dimensional feature fusion and decoding. Results: EEG signals in VR scenes exhibit a significant event-related desynchronization/event-related synchronization phenomenon. Additionally, phase-locked value analysis reveals enhanced functional connectivity in the brain network. Motion intention decoding using C-CTNet achieved classification accuracies of 86.11 % on the BCI Competition IV 2a dataset, 89.53 % on the PhysioNet EEG Motor Movement/Imagery dataset and 92.41 % on a dataset of 10 SCI patients, significantly surpassing traditional methods. Conclusion: The findings confirm that integrating VR enhances EEG signal acquisition quality, while the proposed multi-feature fusion framework improves BCI decoding performance. Significance: This method improves the accuracy of motor intention recognition while enhancing the stability and reliability of EEG signals, thereby providing key support for intelligent, personalized, and immersive neurorehabilitation. It shows promise for speeding functional recovery in SCI patients and laying the groundwork for clinical application and widespread adoption of BCI systems.