Title :
Comparison of spike-sorting algorithms for future hardware implementation
Author :
Sarah Gibson;Jack W. Judy;Dejan Markovic
Author_Institution :
Department of Electrical Engineering, University of California, Los Angeles, USA
Abstract :
Applications such as brain-machine interfaces require hardware spike sorting in order to (1) obtain single-unit activity and (2) perform data reduction for wireless transmission of data. Such systems must be low-power, low-area, high-accuracy, automatic, and able to operate in real time. Several detection and feature extraction algorithms for spike sorting are described briefly and evaluated in terms of accuracy versus computational complexity. The nonlinear energy operator method is chosen as the optimal spike detection algorithm, being most robust over noise and relatively simple. The discrete derivatives method [1] is chosen as the optimal feature extraction method, maintaining high accuracy across SNRs with a complexity orders of magnitude less than that of traditional methods such as PCA.
Conference_Titel :
Engineering in Medicine and Biology Society, 2008. EMBS 2008. 30th Annual International Conference of the IEEE
Print_ISBN :
978-1-4244-1814-5
Electronic_ISBN :
1558-4615
DOI :
10.1109/IEMBS.2008.4650340