Neural spike sorting under nearly 0-dB signal-to-noise ratio using nonlinear energy operator and artificial neural-network classifier

Reports a result on neural spike sorting under conditions where the signal-to-noise ratio is very low. The use of nonlinear energy operator enables the detection of an action potential, even when the SNR is so poor that a typical amplitude thresholding method cannot be applied. The superior detection ability facilitates the collection of a training set under lower SNR than that of the methods which employ simple amplitude thresholding. Thus, the statistical characteristics of the input vectors can be better represented in the neural-network classifier The trained neural-network classifiers yield the correct classification ratio higher than 90% when the SNR is as low as 1.2 (0.8 dB) when applied to data obtained from extracellular recording from Aplysia abdominal ganglia using a semiconductor microelectrode array.

[1]  G. P. Moore,et al.  Neuronal spike trains and stochastic point processes. I. The single spike train. , 1967, Biophysical journal.

[2]  G. P. Moore,et al.  Neuronal spike trains and stochastic point processes. II. Simultaneous spike trains. , 1967, Biophysical journal.

[3]  James C. Bezdek,et al.  Pattern Recognition with Fuzzy Objective Function Algorithms , 1981, Advanced Applications in Pattern Recognition.

[4]  Bruce C. Wheeler,et al.  A Comparison of Techniques for Classification of Multiple Neural Signals , 1982, IEEE Transactions on Biomedical Engineering.

[5]  David J. Anderson,et al.  Solid-State Electrodes for Multichannel Multiplexed Intracortical Neuronal Recording , 1986, IEEE Transactions on Biomedical Engineering.

[6]  Y. Tsividis.  Operation and modeling of the MOS transistor , 1987 .

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

[8]  William Bialek,et al.  Reading a Neural Code , 1991, NIPS.

[9]  Bruce W. Suter,et al.  The multilayer perceptron as an approximation to a Bayes optimal discriminant function , 1990, IEEE Trans. Neural Networks.

[10]  Kenneth O. Johnson,et al.  Automated analyzer for on-line recognition of neural waveforms in extracellular recordings of multiple neurons , 1992, 1992 14th Annual International Conference of the IEEE Engineering in Medicine and Biology Society.

[11]  Douglas L. Jones,et al.  A resolution comparison of several time-frequency representations , 1992, IEEE Trans. Signal Process..

[12]  K. Wise,et al.  An implantable CMOS circuit interface for multiplexed microelectrode recording arrays , 1992 .

[13]  Petros Maragos,et al.  On amplitude and frequency demodulation using energy operators , 1993, IEEE Trans. Signal Process..

[14]  J.P. Miller,et al.  Optimal discrimination and classification of neuronal action potential waveforms from multiunit, multichannel recordings using software-based linear filters , 1994, IEEE Transactions on Biomedical Engineering.

[15]  Carl W. Helstrom,et al.  Elements of signal detection and estimation , 1994 .

[16]  K. Horch,et al.  Classification of action potentials in multi-unit intrafascicular recordings using neural network pattern-recognition techniques , 1994, IEEE Transactions on Biomedical Engineering.

[17]  D. Signorini,et al.  Neural networks , 1995, The Lancet.

[18]  Isaac N. Bankman,et al.  Neural waveform detector for prosthesis control , 1995, Proceedings of 17th International Conference of the Engineering in Medicine and Biology Society.

[19]  F. Öhberg,et al.  A neural network approach to real-time spike discrimination during simultaneous recording from several multi-unit nerve filaments , 1996, Journal of Neuroscience Methods.

[20]  Monson H. Hayes,et al.  Statistical Digital Signal Processing and Modeling , 1996 .

[21]  Erkki Oja,et al.  Neural and statistical classifiers-taxonomy and two case studies , 1997, IEEE Trans. Neural Networks.

[22]  R. Chandra,et al.  Detection, classification, and superposition resolution of action potentials in multiunit single-channel recordings by an on-line real-time neural network , 1997, IEEE Transactions on Biomedical Engineering.

[23]  M S Lewicki,et al.  A review of methods for spike sorting: the detection and classification of neural action potentials. , 1998, Network.

[24]  S. Mukhopadhyay,et al.  A new interpretation of nonlinear energy operator and its efficacy in spike detection , 1998, IEEE Transactions on Biomedical Engineering.

[25]  Sung June Kim,et al.  Noise performance design of CMOS preamplifier for the active semiconductor neural probe , 2000, IEEE Trans. Biomed. Eng..