Predicting 3-Dimensional Arm Trajectories from the Activity of Cortical Neurons for Use in Neural Prosthetics

Introduction Neural prosthetics is a relatively new field that involves recording from neural activity in the cortex and decoding a useful signal that can be used to control an external device. There has been considerable recent interest in using such a system to provide brain controlled robotic limbs to amputees. However, due to the increased safety concerns and cost to patients undergoing neurosurgery, the viability of brain controlled robotic limbs depends on the ability to control many more degrees of freedom than currently available devices, and produce nearly arbitrary movement. While sufficiently articulated robotic limbs can be created, it still an open question whether sufficient control information can be obtained using the output of a few dozen neurons. Significant prior work has been done with animals selecting fixed targets by controlling a cursor on a screen with neural activity. One approach involves decoding the desired end point directly. This approach has resulted in the highest rate of information transfer reported to date, 6.5 bps. [1] However, controlling a robotic limb would require the second approach, which involves decoding trajectories rather than end point. Several groups have taken this approach, including recent work in humans, which utilized a Kalman filter for closed loop control of a cursor on a computer screen. This group and others have also demonstrated the ability for humans and monkeys to control robotic limbs with limited degrees of freedom in similar closed loop systems. [2-3,6] However, a practical clinical system for a brain controlled robotic limb could not involve training on every possible reaching location the patient might require. Therefore, this class project evaluates the ability of several of these decoding algorithms to generalize to targets not seen in the training data set. This will involve driving a two-segment arm model in Matlab with neural data obtained from rhesus macaques making hand reaches to one of eight commanded targets. Several methods were used including linear regression with different position encoding schemes, a Kalman filter using the neural firing rates as a noisy measure of the desired position, and Naïve Bayes encoding discrete movement steps. Several groups still use linear regression to reconstruct end point trajectories due to its ease of implementation and fairly high performance [5,6]. However, it does not constrain the endpoint to realizable arm movements. A Kalman filter does constrain the movements to reasonable trajectories, and represents the current state of the art in human FDA trials for brain computer interfaces [7]. Finally, Naïve Bayes is similar to direct endpoint systems that have the highest reported performance, though extending this into trajectory decoding requires discretizing all aspects of the movement. These different approaches were first compared according to overall ability to generate correct trajectories in this experimental paradigm, and then evaluated based on how well the learned models generalized to targets left out of the training data.