Unsupervised neural spike sorting for high-density microelectrode arrays with convolutive independent component analysis

2016 ◽  
Vol 271 ◽  
pp. 1-13 ◽  
Author(s):  
Christian Leibig ◽  
Thomas Wachtler ◽  
Günther Zeck
Keyword(s):  
Independent Component Analysis ◽  
Microelectrode Arrays ◽  
Component Analysis ◽  
High Density ◽  
Independent Component ◽  
Spike Sorting ◽  
Neural Spike

scholarly journals Automatic spike sorting for high-density microelectrode arrays

2018 ◽  
Vol 120 (6) ◽  
pp. 3155-3171 ◽  
Author(s):  
Roland Diggelmann ◽  
Michele Fiscella ◽  
Andreas Hierlemann ◽  
Felix Franke
Keyword(s):  
Principal Component Analysis ◽  
Clustering Algorithm ◽  
State Of The Art ◽  
Microelectrode Arrays ◽  
Principal Component ◽  
Component Analysis ◽  
High Density ◽  
Sorting Algorithm ◽  
Spike Sorting ◽  
Recording Electrodes

High-density microelectrode arrays can be used to record extracellular action potentials from hundreds to thousands of neurons simultaneously. Efficient spike sorters must be developed to cope with such large data volumes. Most existing spike sorting methods for single electrodes or small multielectrodes, however, suffer from the “curse of dimensionality” and cannot be directly applied to recordings with hundreds of electrodes. This holds particularly true for the standard reference spike sorting algorithm, principal component analysis-based feature extraction, followed by k-means or expectation maximization clustering, against which most spike sorters are evaluated. We present a spike sorting algorithm that circumvents the dimensionality problem by sorting local groups of electrodes independently with classical spike sorting approaches. It is scalable to any number of recording electrodes and well suited for parallel computing. The combination of data prewhitening before the principal component analysis-based extraction and a parameter-free clustering algorithm obviated the need for parameter adjustments. We evaluated its performance using surrogate data in which we systematically varied spike amplitudes and spike rates and that were generated by inserting template spikes into the voltage traces of real recordings. In a direct comparison, our algorithm could compete with existing state-of-the-art spike sorters in terms of sensitivity and precision, while parameter adjustment or manual cluster curation was not required. NEW & NOTEWORTHY We present an automatic spike sorting algorithm that combines three strategies to scale classical spike sorting techniques for high-density microelectrode arrays: 1) splitting the recording electrodes into small groups and sorting them independently; 2) clustering a subset of spikes and classifying the rest to limit computation time; and 3) prewhitening the spike waveforms to enable the use of parameter-free clustering. Finally, we combined these strategies into an automatic spike sorter that is competitive with state-of-the-art spike sorters.

scholarly journals Independent Component Analysis of High-Density Electromyography in Muscle Force Estimation

2007 ◽  
Vol 54 (4) ◽  
pp. 751-754 ◽  
Author(s):  
Didier Staudenmann ◽  
Andreas Daffertshofer ◽  
Idsart Kingma ◽  
Dick F. Stegeman ◽  
Jaap H. van Dieen
Keyword(s):  
Independent Component Analysis ◽  
Muscle Force ◽  
Component Analysis ◽  
High Density ◽  
Independent Component ◽  
Force Estimation
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