Paper Info
Title
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.
Year
DOI
Venue
2011
10.1162/NECO_a_00063
Neural Computation
Keywords
Field
DocType
detection performance,multiscale correlation,detection algorithm,wavelet coefficient,spike detection,new detection algorithm,proposed algorithm,fundamental neural network mechanism,wavelet-based image edge detection,detection result,recorded neural waveform,real neural recording,action potential,neural network,edge detection,brain machine interface
Data set,DECIPHER,Computer science,Edge detection,Waveform,Sampling (statistics),Artificial intelligence,Artificial neural network,Detector,Machine learning,Wavelet
Journal
Volume
Issue
ISSN
23
1
1530-888X
Citations 
PageRank 
References 
6
0.55
13
Authors
3
Name
Order
Citations
PageRank
Chen-Hui Yang15010.88
Byron Olson281.61
Jennie Si374670.23