Towards a Modular Brain-Machine Interface for Intelligent Vehicle Systems Control - A CARLA Demonstration

Objective: Individuals with paralysis often have mobility and dexterity impairments that limit their ability to operate motor vehicle controls. Integrating brain-machine interface (BMI) neurotechnology with vehicle control systems (VCS) provides a novel solution to this problem. In this proof-of-concept study, we show that an intracortical BMI developed to restore voluntary grasp can be repurposed to decode motor intention for vehicle velocity and steering control. Methods: The BMI-VCS consists of four components: 1) implanted motor cortex microelectrode array and NeuroPort data acquisition system, 2) machine learning workstation, 3) Python interface to generate control signals, and 4) vehicle control system. Results: Direct cortical steering and velocity control were achieved through accurate decoding of movement intention (supination, pronation, hand open, hand close) from the participant's motor cortex, translating intention into vehicle commands (turn right, turn left, accelerate, decelerate, respectively), and dynamically switching between commands to turn corners, start and stop, shift from forward to reverse, and parallel park. Conclusion: By translating BMI decoder outputs into high-level vehicle commands, a participant with tetraparesis from C5 ASIA A spinal cord injury successfully navigated CARLA driving simulator courses in real time. These decoder outputs could also be used offline for shared control of a scale model car. Significance: High-level, shared vehicle control with BMI-VCS offers an innovative way to return independent driving abilities to those with disability. BMI systems that can control multiple end-effectors may be particularly useful to those with paralysis