Using template matching and compressed sensing techniques to enhance performance of neural spike detection and data compression systems

Signal processing is an important component in advanced neural recording devices, which facilitates increasing number of neuroscientific investigations. Recently, template matching and compressed sensing (CS) techniques have attracted attention for their use in spike detection and data compression respectively. In this article, we propose an online adaptive digital spike detection and data compression system using Bayesian inference-based template matching and the CS technique. Bayesian inference is applied for spike detection using either pre-generated or automatically generated templates. The proposed module locates the spikes with a high-detection accuracy. Additionally, it integrates the CS technique and applies a sensing matrix, called a Minimum Euclidean or Manhattan Distance Cluster-based (MDC) matrix, to compress signals. Using the MDC matrix, the proposed system cannot only compress a signal with a high-compression rate (CR) but also with a low reconstruction error (RER).