Title :
Optimization of nonlinear energy operator based spike detection circuit for high density neural recordings
Author :
Yuning Yang ; Mason, Andrew J.
Author_Institution :
Electr. & Comput. Eng, Michigan State Univ., East Lansing, MI, USA
Abstract :
Future brain machine interface systems will require recording thousands of neural channels, making it important to minimize the power and area of neural interface integrated circuits. Spike detection is an essential step for neural signal processing. This paper describes the design of a spike detection circuit based on the nonlinear energy operator (NEO) algorithm that is optimized for power and area. Through statistical analysis of NEO coefficients, the number of computations is minimized and the number of registers is shown to be as low as one per channel without degenerating spike detection performance. Based on an analysis of the power-area tradeoff, an optimal 16-channel interleaved architecture is presented and shown to achieve a factor of 4 improvement in power-area product compared to reported NEO implementations.
Keywords :
biological techniques; biomedical electronics; brain-computer interfaces; medical signal detection; network synthesis; neurophysiology; NEO algorithm; NEO coefficients; brain-machine interface systems; circuit area optimisation; high density neural recordings; neural channels; neural interface integrated circuits; neural signal processing; nonlinear energy operator; optimal sixteen-channel interleaved architecture; power consumption minimisation; power-area product; spike detection circuit design; spike detection optimization; spike detection performance; statistical analysis; Accuracy; Computer architecture; Noise level; Optimization; Power demand; Registers; Sorting; VLSI; brain machine interface; spike detection;
Conference_Titel :
Circuits and Systems (ISCAS), 2014 IEEE International Symposium on
Conference_Location :
Melbourne VIC
Print_ISBN :
978-1-4799-3431-7
DOI :
10.1109/ISCAS.2014.6865405
