Michigan State University - Research Groups

This chapter focuses on the joint problem of detection, estimation, and classification of neuronal action potentials in noisy microelectrode recordings—often referred to as spike detection and sorting. The importance of this problem stems from the fact that its outcome affects virtually all subsequent analysis. In the absence of a clear consensus in the community on what constitutes the best method, spike detection and sorting have been and will continue to be a subject of intense research because techniques for multiunit recordings have started to emerge. The chapter provides an in-depth presentation of the fundamentals of detection and estimation theory as applied to this problem. It then offers an overview of traditional and novel methods that revolve around the theory, in particular contrasting the differences—and potential benefits—that arise when detecting and sorting spikes with a single-channel versus a multi-channel recording device. The authors further link multiple aspects of classic and modern signal processing techniques to the unique challenges encountered in the extracellular neural recording environment. Finally, they provide a practical way to perform this task using a computationally efficient, hardware-optimized platform suitable for real-time implementation in neuroprosthetic devices and BMI applications.