PROJECT TITLE :

Adaptive Threshold Neural Spike Detector Using Stationary Wavelet Transform in CMOS

ABSTRACT:

Spike detection is a necessary 1st step in the analysis of neural recordings. Detection at the frontend eases the bandwidth requirement for wireless knowledge transfer of multichannel recordings to further-cranial processing units. In this work, an occasional power digital integrated spike detector primarily based on the lifting stationary wavelet transform is presented and developed. By monitoring the standard deviation of wavelet coefficients, the proposed detector will adaptively set a threshold price online for every channel independently while not requiring user intervention. A prototype 16-channel spike detector was designed and tested in an FPGA. The strategy enables spike detection with nearly 90p.c accuracy even when the signal-to-noise ratio is as low as 2. The planning was mapped to 130 nm CMOS technology and shown to occupy of space and dissipate of power per channel, creating it suitable for implantable multichannel neural recording systems.


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