A Multiscale Correlation of Wavelet Coefficients Approach to Spike Detection

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.

[1]  Fred Wolf,et al.  Onset Dynamics of Action Potentials in Rat Neocortical Neurons and Identified Snail Neurons: Quantification of the Difference , 2008, PloS one.

[2]  M. Volgushev,et al.  Unique features of action potential initiation in cortical neurons , 2006, Nature.

[3]  Hongbin Wang,et al.  A spike sorting framework using nonparametric detection and incremental clustering , 2006, Neurocomputing.

[4]  Joel W. Burdick,et al.  Spike detection using the continuous wavelet transform , 2005, IEEE Transactions on Biomedical Engineering.

[5]  E Hulata,et al.  Detection and sorting of neural spikes using wavelet packets. , 2000, Physical review letters.

[6]  Zoran Nenadic,et al.  Robust Unsupervised Detection of Action Potentials With Probabilistic Models , 2008, IEEE Transactions on Biomedical Engineering.

[7]  Dennis M. Healy,et al.  Wavelet transform domain filters: a spatially selective noise filtration technique , 1994, IEEE Trans. Image Process..

[8]  J. Si,et al.  Closed-loop cortical control of direction using support vector machines , 2005, IEEE Transactions on Neural Systems and Rehabilitation Engineering.

[9]  Karim G. Oweiss,et al.  A multiresolution generalized maximum likelihood approach for the detection of unknown transient multichannel signals in colored noise with unknown covariance , 2002, 2002 IEEE International Conference on Acoustics, Speech, and Signal Processing.

[10]  Joel W. Burdick,et al.  A control algorithm for autonomous optimization of extracellular recordings , 2006, IEEE Transactions on Biomedical Engineering.

[11]  David J. Anderson,et al.  A unified framework for advancing array signal processing technology of multichannel microprobe neural recording devices , 2002, 2nd Annual International IEEE-EMBS Special Topic Conference on Microtechnologies in Medicine and Biology. Proceedings (Cat. No.02EX578).

[12]  Brian M. Sadler,et al.  Analysis of Multiscale Products for Step Detection and Estimation , 1999, IEEE Trans. Inf. Theory.

[13]  S. Mallat.  A wavelet tour of signal processing , 1998 .

[14]  Sung June Kim,et al.  A wavelet-based method for action potential detection from extracellular neural signal recording with low signal-to-noise ratio , 2003, IEEE Transactions on Biomedical Engineering.

[15]  P. G Musial,et al.  Signal-to-noise ratio improvement in multiple electrode recording , 2002, Journal of Neuroscience Methods.

[16]  Matthew Fellows,et al.  On the variability of manual spike sorting , 2004, IEEE Transactions on Biomedical Engineering.

[17]  X. Yang,et al.  A totally automated system for the detection and classification of neural spikes , 1988, IEEE Transactions on Biomedical Engineering.

[18]  George L. Gerstein,et al.  A low-cost single-board solution for real-time, unsupervised waveform classification of multineuron recordings , 1989, Journal of Neuroscience Methods.

[19]  José Carlos Príncipe,et al.  An Associative Memory Readout in ESN for Neural Action Potential Detection , 2007, 2007 International Joint Conference on Neural Networks.

[20]  R. Segev,et al.  A method for spike sorting and detection based on wavelet packets and Shannon's mutual information , 2002, Journal of Neuroscience Methods.

[21]  R. Hallin,et al.  Multiple action potential waveforms of single units in man as signs of variability in conductivity of their myelinated fibres , 1996, Brain Research.

[22]  Lei Zhang,et al.  Noise Reduction for Magnetic Resonance Images via Adaptive Multiscale Products Thresholding , 2003, IEEE Trans. Medical Imaging.

[23]  R. Quian Quiroga,et al.  Unsupervised Spike Detection and Sorting with Wavelets and Superparamagnetic Clustering , 2004, Neural Computation.

[24]  Giovanni Fiengo,et al.  A biophysically inspired microelectrode recording-based model for the subthalamic nucleus activity in Parkinson's disease , 2008, Biomed. Signal Process. Control..

[25]  Hanzhang Lu,et al.  Automated optimal detection and classification of neural action potentials in extra-cellular recordings , 2007, Journal of Neuroscience Methods.

[26]  D. Humphrey,et al.  Extracellular Single-Unit Recording Methods , 1990 .

[27]  D. Kleinfeld,et al.  Variability of extracellular spike waveforms of cortical neurons. , 1996, Journal of neurophysiology.