A novel unsupervised spike sorting algorithm for intracranial EEG

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.

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Complexity Optimization and High-Throughput Low-Latency Hardware Implementation of a Multi-Electrode Spike-Sorting Algorithm

IEEE Transactions on Neural Systems and Rehabilitation Engineering ◽

10.1109/tnsre.2014.2370510 ◽

2015 ◽

Vol 23 (2) ◽

pp. 149-158 ◽

Cited By ~ 11

Author(s):

Jelena Dragas ◽

David Jackel ◽

Andreas Hierlemann ◽

Felix Franke

Keyword(s):

High Throughput ◽

Hardware Implementation ◽

Low Latency ◽

Sorting Algorithm ◽

Spike Sorting

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A novel and fully automatic spike-sorting implementation with variable number of features

Journal of Neurophysiology ◽

10.1152/jn.00339.2018 ◽

2018 ◽

Vol 120 (4) ◽

pp. 1859-1871 ◽

Cited By ~ 30

Author(s):

Fernando J. Chaure ◽

Hernan G. Rey ◽

Rodrigo Quian Quiroga

Keyword(s):

Single Channel ◽

Feature Selection Method ◽

Simulated Data ◽

Variable Number ◽

False Positives ◽

Sorting Algorithm ◽

Spike Sorting ◽

Fully Automatic ◽

Multiple Clusters ◽

Tetrode Recordings

The most widely used spike-sorting algorithms are semiautomatic in practice, requiring manual tuning of the automatic solution to achieve good performance. In this work, we propose a new fully automatic spike-sorting algorithm that can capture multiple clusters of different sizes and densities. In addition, we introduce an improved feature selection method, by using a variable number of wavelet coefficients, based on the degree of non-Gaussianity of their distributions. We evaluated the performance of the proposed algorithm with real and simulated data. With real data from single-channel recordings, in ~95% of the cases the new algorithm replicated, in an unsupervised way, the solutions obtained by expert sorters, who manually optimized the solution of a previous semiautomatic algorithm. This was done while maintaining a low number of false positives. With simulated data from single-channel and tetrode recordings, the new algorithm was able to correctly detect many more neurons compared with previous implementations and also compared with recently introduced algorithms, while significantly reducing the number of false positives. In addition, the proposed algorithm showed good performance when tested with real tetrode recordings. NEW & NOTEWORTHY We propose a new fully automatic spike-sorting algorithm, including several steps that allow the selection of multiple clusters of different sizes and densities. Moreover, it defines the dimensionality of the feature space in an unsupervised way. We evaluated the performance of the algorithm with real and simulated data, from both single-channel and tetrode recordings. The proposed algorithm was able to outperform manual sorting from experts and other recent unsupervised algorithms.

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D.sort: template based automatic spike sorting tool

10.1101/423913 ◽

2018 ◽

Author(s):

Madeny Belkhiri ◽

Duda Kvitsiani

Keyword(s):

Template Matching ◽

Ground Truth ◽

Sorting Algorithm ◽

Processing Unit ◽

Spike Sorting ◽

Ground Truth Data ◽

Objective Metrics ◽

Movement Artifacts ◽

Freely Behaving ◽

The Individual

AbstractUnderstanding how populations of neurons represent and compute internal or external variables requires precise and objective metrics for tracing the individual spikes that belong to a given neuron. Despite recent progress in the development of accurate and fast spike sorting tools, the scarcity of ground truth data makes it difficult to settle on the best performing spike sorting algorithm. Besides, the use of different configurations of electrodes and ways to acquire signal (e.g. anesthetized, head fixed, freely behaving animal recordings, tetrode vs. silicone probes, etc.) makes it even harder to develop a universal spike sorting tool that will perform well without human intervention. Some of the prevalent problems in spike sorting are: units separating due to drift, clustering bursting cells, and dealing with nonstationarity in background noise. The last is particularly problematic in freely behaving animals where the noises from the electrophysiological activity of hundreds or thousands of neurons are intermixed with noise arising from movement artifacts. We address these problems by developing a new spike sorting tool that is based on a template matching algorithm. The spike waveform templates are used to perform normalized cross correlation (NCC) with an acquired signal for spike detection. The normalization addresses problems with drift, bursting, and nonstationarity of noise and provides normative scoring to compare different units in terms of cluster quality. Our spike sorting algorithm, D.sort, runs on the graphic processing unit (GPU) to accelerate computations. D.sort is a freely available software package (https://github.com/1804MB/Kvistiani-lab_Dsort).

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ION-Decoding: A Single-channel Interactive Offline Neural Decoding Algorithm for a large number of neurons

10.1101/2020.11.16.385583 ◽

2020 ◽

Author(s):

Mohsen Rastegari ◽

Hamid Reza Marateb

Keyword(s):

Single Channel ◽

Signal To Noise Ratio ◽

Sorting Algorithm ◽

Spike Sorting ◽

Decoding Algorithm ◽

False Positive Error ◽

Extracellular Recordings ◽

Health Technologies ◽

Cognitive Studies ◽

Minimum Number

AbstractResearchers have widely used extracellular recordings as a technique of paramount importance due to its wide usage in cognitive studies, health technologies, and prosthetics and orthotics research. To extract the required information from this technique, a critical and crucial step, called spike sorting, must be performed on the recorded signal. By this method, it is possible to analyze a single neuron (single-unit activity) and investigate its specifications, such as the firing rates and the number of action potentials (spikes) of an individual neuron. Here we introduce a novel idea of a user-friendly interactive, offline, and unsupervised algorithm called ION-Decoding. This platform extracts and aligns the spikes using a high-resolution alignment method, and the clusters can be atomically identified and manually edited. The entire procedure is performed using the minimum number of adjustable parameters, and cluster merging was performed in a smart, intuitive way. The ION-Decoding algorithm was evaluated by a benchmark dataset, including 95 simulations of two to twenty neurons from 10 minutes simulated extracellular recordings. There was not any significant relationship between the number of missed clusters with the quality of the signal (i.e., the signal-to-noise ratio (SNR)) by controlling the number of neurons in each signal (p_value=0.103). Moreover, the number of extra clusters was not significantly dependent on the parameter SNR (p_value=0.400). The accuracy of the classification method was significantly associated with the decomposability index (DI) (p_value<0.001). A number of 77% of the neurons with the DI higher than 20 had the classification accuracy higher than 80%. The ION-Decoding algorithm significantly outperformed Wave_Clus in terms of the number of hits (p_value=0.017). However, The Wave_Clus algorithm significantly outperformed the ION-Decoding algorithm when the false-positive error (FP) was considered (p_value=0.001). The ION-Decoding is thus a promising single-channel spike sorting algorithm. However, our future focuses on the improvement of the cluster representative identification and FP error reduction.

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SHYBRID: A graphical tool for generating hybrid ground-truth spiking data for evaluating spike sorting performance

10.1101/734061 ◽

2019 ◽

Cited By ~ 4

Author(s):

Jasper Wouters ◽

Fabian Kloosterman ◽

Alexander Bertrand

Keyword(s):

Ground Truth ◽

Neural Recording ◽

Data Driven ◽

Sorting Algorithm ◽

Spike Sorting ◽

Recording Device ◽

Ground Truth Data ◽

Sorting Algorithms ◽

Graphical Tool ◽

Fine Tune

AbstractSpike sorting is the process of retrieving the spike times of individual neurons that are present in an extracellular neural recording. Over the last decades, many spike sorting algorithms have been published. In an effort to guide a user towards a specific spike sorting algorithm, given a specific recording setting (i.e., brain region and recording device), we provide an open-source graphical tool for the generation of hybrid ground-truth data in Python. Hybrid ground-truth data is a data-driven modelling paradigm in which spikes from a single unit are moved to a different location on the recording probe, thereby generating a virtual unit of which the spike times are known. The tool enables a user to efficiently generate hybrid ground-truth datasets and make informed decisions between spike sorting algorithms, fine-tune the algorithm parameters towards the used recording setting, or get a deeper understanding of those algorithms.

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Online spike sorting via deep contractive autoencoder

10.1101/2021.04.23.441225 ◽

2021 ◽

Author(s):

Mohammadreza Radmanesh ◽

Ahmad Asgharian Rezaei ◽

Alireza Hashemi ◽

Mahdi Jalili ◽

Morteza Moazami Goudarzi

Keyword(s):

Neuron Activity ◽

Sorting Algorithm ◽

Spike Sorting ◽

Small Perturbations ◽

Loop Control ◽

Neural Data ◽

Online Classification ◽

Neuroscience Research ◽

Spike Waveforms

AbstractSpike sorting – the process of separating spikes from different neurons – is often the first and most critical step in the neural data analysis pipeline. Spike-sorting techniques isolate a single neuron’s activity from background electrical noise based on the shapes of the waveforms (WFs) obtained from extracellular recordings. Despite several advancements in this area, an important remaining challenge in neuroscience is online spike sorting, which has the potential to significantly advance basic neuroscience research and the clinical setting by providing the means to produce real-time perturbations of neurons via closed-loop control. Current approaches to online spike sorting are not fully automated, are computationally expensive and are often outperformed by offline approaches. In this paper, we present a novel algorithm for fast and robust online classification of single neuron activity. This algorithm is based on a deep contractive autoencoder (DCAE) architecture. DCAEs are deep neural networks that can learn a latent state representation of their inputs. The main advantage of DCAE approaches is that they are less sensitive to noise (i.e., small perturbations in their inputs). We therefore reasoned that they can form the basis for robust online spike sorting algorithms. Overall, our DCAE-based online spike sorting algorithm achieves over 90% accuracy at sorting previously-unseen spike waveforms. Moreover, our approach produces superior results compared to several state-of-the-art offline spike-sorting procedures.

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