Model-based decoding of reaching movements for prosthetic systems

Model-based decoding of neural activity for neuroprosthetic systems has been shown, in simulation, to provide significant gain over traditional linear filter approaches. We tested the model-based decoding approach with real neural and behavioral data and found a 18% reduction in trajectory reconstruction error compared with a linear filter. This corresponds to a 40% reduction in the number of neurons required for equivalent performance. The model-based approach further permits the combination of target-tuned plan activity with movement activity. The addition of plan activity reduced reconstruction error by 23% relative to the linear filter, corresponding to 55% reduction in the number of neurons required. Taken together, these results indicate that a decoding algorithm employing a prior model of reaching kinematics can substantially improve trajectory estimates, thereby improving prosthetic system performance.