BrainVLA: Towards VLA-driven High-level Embodied Planning for Motor Brain-Computer Interfaces

Intracortical Brain-Computer Interfaces (iBCIs) restore motor function for paralyzed patients by decoding neural signals into control commands. However, current decoding models lack environmental awareness and require users to continuously control spatial trajectories, causing cognitive fatigue and compounding control errors. To address this limitation, we introduce BrainVLA, a hybrid-intelligence framework that integrates iBCIs with pre-trained Vision-Language-Action (VLA) models by leveraging neural signals as high-level planning priors. BrainVLA extracts intent embeddings from neural activity during motor preparation and projects them into the VLA model's semantic space. This integration establishes a control hierarchy in which neural signals dictate high-level goals, leaving the VLA model to leverage visual context for fine-grained action generation. Experiments on the maze reach task demonstrate that BrainVLA achieves a 31.6% higher success rate than classical neural decoders on average. It outperforms the VLA model ${\pi }_0$ by 17.7% in unseen scenarios, highlighting its impressive generalizability. Our approach establishes a new embodied iBCI paradigm where robotic agents harness cognitive abilities from biological systems.