A wavelet based Teager energy operator for spike detection in microelectrode array recordings

Abstract

Spike detection in neural recordings is the initial step in the creation of brain machine interfaces. The Teager energy operator (TEO) treats a spike as an increase in the 'local' energy and detects this increase. The performance of TEO in detecting action potential spikes suffers due to its sensitivity to the frequency of spikes in the presence of noise which is present in microelectrode array (MEA) recordings. The multiresolution TEO (mTEO) method overcomes this shortcoming of the TEO by tuning the parameter k to an optimal value m so as to match to frequency of the spike.In this paper, we present an algorithm for the mTEO using the multiresolution structure of wavelets along with inbuilt lowpass filtering of the subband signals. The algorithm is efficient and can be implemented for real-time processing of neural signals for spike detection. The performance of the algorithm is tested on a simulated neural signal with 10 spike templates obtained from [14]. The background noise is modeled as a colored Gaussian random process. Using the noise standard deviation and autocorrelation functions obtained from recorded data, background noise was simulated by an autoregressive (AR(5)) filter. The simulations show a spike detection accuracy of 90% and above with less than 5% false positives at an SNR of 2.35 dB as compared to 80% accuracy and 10% false positives reported [6] on simulated neural signals.

A stationary wavelet transform and a time-frequency based spike detection algorithm for extracellular recorded data

“…The transients introduce a frequency pattern with a distinct characteristic, different from noise. For example, the nonlinear Teager energy operators in [6,10,11,14] fall into this category, as well as [16,19]. Further, both algorithms proposed in this paper belong to this category.…”

“…This newly proposed algorithm is based on a further development of the WTEO algorithm from Nabar et al in [10] and hence also belongs to Category C in section 1. Their spike detection algorithm is based on a low-pass filter using the first and second level approximation coefficients of the DWT.…”

“…The SWTTEO algorithm adopts the following changes to improve the WTEO algorithm. The discrete wavelet transform used in [10] is substituted by the stationary wavelet transform. Hence, no down sampling of the wavelet approximation coefficients is applied, superseding the need to expand decimated coefficients.…”

“…For each low-pass sub-band we compute the TEO, convolve each independently with a simple smoothing window and sum the results. The thresholding is then not applied to the individual sub-bands itself (as suggested in [10]), but rather at the combined output. A block diagram of the SWTTEO algorithm is shown in figure 5(b).…”

“…Several algorithms for spike detection have already been published [1,[5][6][7][8][9][10][11][12][13][14][15][16][17][18][19]. Based on their methodological concept most of them can be divided into three categories (as suggested in [1]):…”