Wavelet-based neural pattern analyzer for behaviorally significant burst pattern recognition

Closed-loop neural prosthesis systems rely on accurately recording neural data from multiple neurons and detecting behaviorally meaningful patterns before representing them in a highly compressed form for wireless transmission over a limited-bandwidth link. We present a novel wavelet-based approach for detecting spikes, grouping them as bursts and building a dynamic vocabulary of meaningful burst patterns. Simulation results on pre-recorded in vivo multi-channel extracellular neural data from the buccal ganglion of Aplysia demonstrate the feasibility of behavior recognition as well as data compression (> 500X) by the proposed approach.