Unsupervised spike sorting with ICA and its evaluation using GENESIS simulations

Mamlouk, Amir Madany; Sharp, Hannah; Menne, Kerstin M. L.; Hofmann, Ulrich G.; Martinetz, Thomas

doi:10.1016/j.neucom.2004.10.019

Cited by 20 publications

(6 citation statements)

References 5 publications

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“…Finding the number of neurons is usually done manually during spike sorting-it is an unsupervised clustering problem where the number of the clusters is unknown. Approaches and solutions for both problems are difficult to evaluate quantitatively, due to a lack of knowledge about the truth behind the experimental data [14].…”

Section: Spike Detection and Sortingmentioning

confidence: 99%

Analysis of Neural Activity of the Human Basal Ganglia in Dystonia: a Review

Klempíř

Krupička

2019

Clin Tech

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Deep brain stimulation of the globus pallidus internus is an efective symptomatic treatment for pharmacoresistant dystonic syndromes, where pathophysiological mechanisms of action are not yet fully understood. The aim of this review article is to provide an overview of the state-of-the-art approaches for processing of microelectrode recordings in dystonia; in order to define biomarkers to identify patients who will benefit from the clinical deep brain stimulation. For this purpose, the essential elements of microelectrode processing are examined. Next, we investigate a real example of spike sorting processing in this field. Herein, we describe baseline elements of microrecordings processing including data collection, preprocessing phase, features computation, spike detection and sorting and finally, advanced spike train data analysis. This study will help readers acquire the necessary information about these elements and their associated techniques. Thus, this study is supposed to assist during identification and proposal of interesting clinical hypotheses in the field of single unit neuronal recordings in dystonia.

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Section: Spike Detection and Sortingmentioning

confidence: 99%

Analysis of Neural Activity of the Human Basal Ganglia in Dystonia: a Review

Klempíř

Krupička

2019

Clin Tech

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“…We consider only a single-channel recording, but the spikesorting algorithms that are commonly applied on singlechannel recordings are applicable on multi-channel recordings as well, usually as a combination with some source separation methods (for examples see [4] and [5]). Say we have a single-channel extracellular neural recording that consists of N samples and let each spike have a duration of M samples.…”

Section: Common Approachmentioning

confidence: 99%

A non-parametric method for automatic neural spike clustering based on the non-uniform distribution of the data

Tiganj¹,

Mboup²

2011

J. Neural Eng.

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In this paper, we propose a simple and straightforward algorithm for neural spike sorting. The algorithm is based on the observation that the distribution of a neural signal largely deviates from the uniform distribution and is rather unimodal. The detected spikes to be sorted are first processed with some feature extraction technique, such as PCA, and then represented in a space with reduced dimension by keeping only a few most important features. The resulting space is next filtered in order to emphasis the differences between the centers and the borders of the clusters. Using some prior knowledge on the lowest level activity of a neuron, such as e.g. the minimal firing rate, we find the number of clusters and the center of each cluster. The spikes are then sorted using a simple greedy algorithm which grabs the nearest neighbors. We have tested the proposed algorithm on real extracellular recordings and used the simultaneous intracellular recordings to verify the results of the sorting. The results suggest that the algorithm is robust and reliable and it compares favorably with the state-of-the-art approaches. The proposed algorithm tends to be conservative, it is simple to implement and is thus suitable for both research and clinical applications as an interesting alternative to the more sophisticated approaches.

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“…Generally, there are thousands of neurons in human body, and the spike waveforms generated by different neutrons are different, which is the physiological basis for spike sorting [1]. Several methods of spike sorting have been put forward in the past work, such as clustering analysis [1,2], artificial neural network [3,4], templates matching [5], SVM(Support Vector Machine) [6][7][8], ICA(Independent Component Analysis) [9] ,etc. The classification results are often unsatisfactory when these methods are directly used to raw spike data.…”

Section: Introductionmentioning

confidence: 99%

A Robust Method for Spike Sorting with Overlap Decomposition

Dai

Luo²

2014

JCP

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It is difficult to identify the spikes to different classifications especially when the neurons have many similar spike waveforms or lots of overlapped spikes. Our previous study proposed the window-slope representation (WSR), and it improved the classification accuracy of high similar spike waveforms. The classification accuracy of the method, however, will be affected by lots of overlapped spikes or low signal-to-noise ratio. In this paper, the secondorder difference method is introduced to solve those problems. First the second-order difference of every spike is calculated to describe the convexity-concavity of the waveform. Then we use the window-slope representation to describe the tendency of waveform at each time. The method is tested at various signal-to-noise ratio levels based on simulation data coming from the Wave_clus. The experiment results show that the accuracy of classification can be improved by using the method together with K-means Cluster. In our experiments, the classification accuracy is above 95% on all datasets in Wave_clus, even under the low signal-to-noise ratio. It shows that this method has a strong robustness.

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Unsupervised spike sorting with ICA and its evaluation using GENESIS simulations

Cited by 20 publications

References 5 publications

Analysis of Neural Activity of the Human Basal Ganglia in Dystonia: a Review

Analysis of Neural Activity of the Human Basal Ganglia in Dystonia: a Review

A non-parametric method for automatic neural spike clustering based on the non-uniform distribution of the data

A Robust Method for Spike Sorting with Overlap Decomposition

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