Enabling a real-time solution for neuron detection with reconfigurable hardware

FPGAs provide a speed advantage in processing for embedded systems, especially when processing is moved close to the sensors. Perhaps the ultimate embedded system is a neural prosthetic, where probes are inserted into the brain and recorded electrical activity is analyzed to determine which neurons have fired. In turn, this information can be used to manipulate an external device such as a robot arm or a computer mouse. To make the detection of these signals possible, some baseline data must be processed to correlate impulses to particular neurons. One method for processing this data uses a statistical clustering algorithm called expectation maximization, or EM. In this paper, we examine the EM clustering algorithm, determine the most computationally intensive portion, map it onto a reconfigurable device, and show several areas of performance gain.