Signal Processing Challenges for Neural Prostheses

Cortically controlled prostheses are able to translate neural activity from the cerebral cortex into control signals for guiding computer cursors or prosthetic limbs. While both noninvasive and invasive electrode techniques can be used to measure neural activity, the latter promises considerably higher levels of performance and therefore functionality to patients. The process of translating analog voltages recorded at the electrode tip into control signals for the prosthesis requires sophisticated signal acquisition and processing techniques. In this article we briefly review the current state-of-the-art in invasive, electrode-based neural prosthetic systems, with particular attention to the advanced signal processing algorithms that enable that performance. Improving prosthetic performance is only part of the challenge, however. A clinically viable prosthetic system will need to be more robust and autonomous and, unlike existing approaches that depend on multiple computers and specialized recording units, must be implemented in a compact, implantable prosthetic processor (IPP). In this article we summarize recent results which indicate that state-of-the-art prosthetic systems can be implemented in an IPP using current semiconductor technology, and the challenges that face signal processing engineers in improving prosthetic performance, autonomy and robustness within the restrictive constraints of the IPP.