<p>Motor imagery (MI) is widely used in brain–computer interfaces (BCIs) due to its simplicity and reproducibility, enabling individuals with motor impairments to perform non-muscular limb training for the rehabilitation of motor-related neurons. While MI-based BCIs have shown promise for neurorehabilitation, current 2D paradigms fail to engage critical sensorimotor networks. To address this limitation, we designed an immersive MI paradigm in a virtual reality (VR) environment, where participants imagined limb movements in response to continuous three-dimensional (3D) palm motion stimuli. Furthermore, we proposed a novel decoding algorithm that integrates depthwise separable convolution with multi-head self-attention mechanisms. The proposed method was evaluated against existing approaches, demonstrating superior classification accuracy while reducing the temporal and spatial complexity associated with attention mechanisms. To assess the generalizability and robustness of the algorithm across different scenarios, we conducted experiments on two publicly available datasets: BCI Competition IV-2a and the PhysioNet MI dataset. Results showed that our method achieved an average increase of nearly 8% in kappa score over EEGNet in decoding four-class MI tasks in 2D paradigms. Consistent performance across both VR and 2D paradigms confirmed the algorithm’s effectiveness and applicability in multi-scenario MI decoding. This study introduces a novel immersive MI paradigm and decoding framework, offering a promising approach for enhancing user engagement in neurorehabilitation and advancing EEG-based intention recognition in VR environments.</p>