Brain-Computer Interfaces (BCIs) enable direct communication between the brain and external devices, offering significant potential for rehabilitation and assistive technologies. A major challenge is the performance gap between BCI training and real-time control due to the variability of the underlying brain signals measured using EEG and user mental strategies. Virtual Reality (VR) plays a crucial role in BCI training by providing immersive, embodied feedback, enhancing user engagement, and potentially improving performance. This study investigates how closed-loop BCI systems, based on motor imagery (MI) of left and right-hand movements, with different VR feedback modalities-comparing true vs. positive feedback-impact EEG features like Event-Related Desynchronization (ERD) and BCI performance. Fifteen participants performed MI BCI training and control, in a VR environment, designed to induce increased sense of embodiment. Our results show consistent classification accuracy and ERD levels across both feedback conditions, indicating that VR feedback, when embodying a virtual body, whether true or positive, supports stable BCI performance. Additionally, key discriminative features emerged outside conventional ERD regions, highlighting the value of exploring non-traditional EEG features for MI task differentiation. This study underscores the importance of VR in optimizing closed-loop BCI systems and improving understanding of neurophysiological responses during MI tasks.