A Multiscale Correlation of Wavelet Coefficients Approach to Spike Detection

Abstract

Extracellular chronic recordings have been used as important evidence in neuroscientific studies to unveil the fundamental neural network mechanisms in the brain. Spike detection is the first step in the analysis of recorded neural waveforms to decipher useful information and provide useful signals for brain-machine interface applications. The process of spike detection is to extract action potentials from the recordings, which are often compounded with noise from different sources. This study proposes a new detection algorithm that leverages a technique from wavelet-based image edge detection. It utilizes the correlation between wavelet coefficients at different sampling scales to create a robust spike detector. The algorithm has one tuning parameter, which potentially reduces the subjectivity of detection results. Both artificial benchmark data sets and real neural recordings are used to evaluate the detection performance of the proposed algorithm. Compared with other detection algorithms, the proposed method has a comparable or better detection performance. In this letter, we also demonstrate its potential for real-time implementation.

Cortical neural responses to previous trial outcome during learning of a directional choice task

“…Offline spike detection and sorting was performed using the M-Sorter from our own laboratory (Yuan et al 2012). The sorter utilizes the multiscale correlation of wavelet coefficients (MCWC) for spike detection (Yang et al 2011). Then, the k-means clustering and template matching algorithms were used to classify single units.…”

The M-Sorter: An automatic and robust spike detection and classification system

“…The multiple correlation of wavelet coefficients (MCWCs) is a high performance spike detection algorithm (Yang et al, 2011;Yuan et al, 2009 detection accuracy and low false positives, as well as few free parameters. Let x(t) be a neural waveform, J be the width of the observation window of the waveform under consideration which is used as the integration interval in the calculation of wavelet coefficients.…”

“…Let H 0 be the null hypothesis that within the window of width J, x(t) does not contain any neural spikes, and let H 1 be the alternative hypothesis that within the window of width J, x(t) contains a spike at b j . Or in other words, the hypothesis test for the original MCWC (Yang et al, 2011) is:…”

“…This consequently creates a great challenge for waveform feature extraction to be used in many of the above mentioned spike sorters. Some advanced wavelet based detection techniques have been proposed to improve detection accuracy (Nenadic and Burdick, 2005;Kim and Kim, 2003;Yang et al, 2011). Some reviews and comparisons of different detection algorithms are available in Yang et al (2011).…”

“…The proposed sorter is based on multiple correlation of wavelet coefficients (MCWCs) (Yang et al, 2011) augmented with thresholding for detection, and template matching for classification. We will show that this approach significantly reduces false positives in the detection step and the overall detection and classification accuracy remains high.…”