Adaptive threshold spike detection using stationary wavelet transform for neural recording implants

Abstract

Spike detection is an essential first step in the analysis of neural recording signals. A new spike detection hardware architecture combining absolute threshold method and stationary wavelet transform (SWT) is described. The method enables spike detection with 90% accuracy even when the signal-to-noise is −1dB. A noise monitoring block was implemented to automatically calculate the appropriate threshold value for spike detection, and the system then chooses either absolute threshold method or the SWT method to optimize power consumption. The system was designed in 130nm CMOS and shown to occupy 0.082 mm <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> and dissipate 0.45 μW for one channel.