A New Action Potential Detector Using the MTEO and Its Effects on Spike Sorting Systems at Low Signal-to-Noise Ratios

Abstract

This paper considers neural signal processing applied to extracellular recordings, in particular, unsupervised action potential detection at a low signal-to-noise ratio. It adopts the basic framework of the multiresolution Teager energy operator (MTEO) detector, but presents important new results including a significantly improved MTEO detector with some mathematical analyses, a new alignment technique with its effects on the whole spike sorting system, and a variety of experimental results. Specifically, the new MTEO detector employs smoothing windows normalized by noise power derived from mathematical analyses and has an improved complexity by utilizing the sampling rate. Experimental results prove that this detector achieves higher detection ratios at a fixed false alarm ratio than the TEO detector and the discrete wavelet transform detector. We also propose a method that improves the action potential alignment performance. Observing that the extreme points of the MTEO output are more robust to the background noise than those of the action potentials, we use the MTEO output for action potential alignment. This brings not only noticeable improvement in alignment performance but also quite favorable influence over the classification performance. Accordingly, the proposed detector improves the performance of the whole spike sorting system. We verified the improvement using various modeled neural signals and some real neural recordings.

Midbrain Synchrony to Envelope Structure Supports Behavioral Sensitivity to Single-Formant Vowel-Like Sounds in Noise

“…Spikes were detected in multiunit neurophysiological recordings after high-pass filtering at 100 Hz (500-point FIR) to minimize the local field potential and transformation of the signal with the multiresolution Teager energy operator (Kim and Kim 2000; Choi et al 2006). Representative transformed neural recordings are shown in a previous publication (see Fig.…”

A unified framework and method for automatic neural spike identification

“…1: (1) detection of temporal segments of the voltage trace that are likely to contain spikes, (2) estimation of a set of features for each segment, and (3) classification of the segments according to these features. A variety of methods exist for solving each step (e.g., (1) thresholding based on absolute value (Obeid and Wolf, 2004), squared values (Rutishauser et al, 2006), Teager energy (Choi et al, 2006), or other nonlinear operators (Rebrik et al, 1999), (2) features such as peak-to-peak width/amplitude, projections onto principal components (Lewicki, 1998), or wavelet coefficients (Quiroga et al, 2004;Kwon and Oweiss, 2011), and (3) classification methods such as K-means (Lewicki, 1998), mixture models (Sahani, 1999;Shoham et al, 2003), or superparamagnetic methods (Quiroga et al, 2004)). …”

Three-Dimensional Carbon Nanotube Electrodes for Extracellular Recording of Cardiac Myocytes

“…This can be explained by the fact that other noise sources like amplifier and voltage source dominate the overall performance at low electrode impedances. Here, we define the SNR as the ratio of squared peak amplitude of the action potential to the variance of the noise SNR = (v peak /r noise ) 2 , which is similar to the definition in Choi et al [4]. With the values given above SNRs of 10 for gold, 207 for TiN and 2,030 for CNT are calculated.…”