Spike detection in extracellular recordings by hybrid blind beamforming

Abstract

In the case of extracellular recordings, spike detection algorithms are necessary in order to retrieve information about neuronal activity form the data. We present a new spike detection algorithm which is based on methods from the field of blind equalization and beamforming. In contrast to existing approaches, our method estimates several waveforms directly from the data and corresponding linear filters are constructed. The estimation is done in an unsupervised manner, and the few parameters of the algorithm are intuitive to set. The algorithm allows for superior detection performance, even when multiple neurons with various waveforms are present in the data. We compare our method with current state-of-the-art spike detection algorithms, and show that the proposed method achieves favorable results.

An Unsupervised and Drift-Adaptive Spike Detection Algorithm Based on Hybrid Blind Beamforming

“…Because we use techniques from both fields, that is, blind equalization and classical beamforming, in the context of spike detection, we call our method hybrid blind beamforming for spike detection (HBBSD). A simplified version of the core algorithm and some preliminary results were published in [25]. This contribution not only extends both in several ways but also presents a significant amount of new algorithms and results.…”

“…In [25] we compared the performance of HBBSD with existing methods covering all categories described in Section 1. It was demonstrated that, in general, algorithms relying on waveform information outperform methods which are solely based on amplitude thresholding or transient detection.…”

“…In Figure 5 the results for all compared methods are shown. (This evaluation is quite short, since results on datasets containing one and two neurons were already presented in [25].) HBBSD achieves a clearly better performance than the competing methods, since it calculates several filters.…”

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

“…This either requires knowledge of the spike's waveform or generic approaches to find suitable templates. Such algorithms are described for example in [8,9,15,17].…”

“…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]):…”