Brain-machine interfaces in rat motor cortex: implications of adaptive decoding algorithms

Construction of a direct brain-machine interface (BMI) for neuroprosthetic purposes is at the forefront of many current neural engineering thrusts. Due to recent breakthroughs in device technology and implantation techniques, a basic framework is now sufficiently developed to allow design of systems level interface strategies producing robust, scalable BMIs that adapt quickly to optimize information transfer at the interface. It has been postulated that knowledge of the underlying neural coding is mandatory for further BMI development. In this preliminary report we use an adaptive algorithm requiring limited knowledge of the underlying neural coding to allow naive rats implanted with Michigan silicon microelectrode arrays in motor cortex to perform a tone discrimination task via differential modulation of the recorded signals. One subject was able to perform the task consistently above chance, despite minor daily fluctuations in recording populations and signal quality. The brain rapidly changed response strategies to facilitate performance of the task, and the algorithm subsequently adapted to accommodate improved BMI operation.