ICA feature extraction for spike sorting of single-channel records

Lopes, Marcus Vinicius; Aguiar, Everton Luiz de; Santana, Ewaldo Éder Carvalho; Barros, Allan Kardec

doi:10.1109/brc.2013.6487468

Cited by 6 publications

(2 citation statements)

References 17 publications

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“…ICA is a linear unsupervised technique for dimensionality reduction that searches for independent components by relying on the statistical properties of the data. ICA has been previously applied to spike sorting with promising results [33,34].…”

Section: State Of the Artmentioning

confidence: 99%

A study of autoencoders as a feature extraction technique for spike sorting

et al. 2023

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Spike sorting is the process of grouping spikes of distinct neurons into their respective clusters. Most frequently, this grouping is performed by relying on the similarity of features extracted from spike shapes. In spite of recent developments, current methods have yet to achieve satisfactory performance and many investigators favour sorting manually, even though it is an intensive undertaking that requires prolonged allotments of time. To automate the process, a diverse array of machine learning techniques has been applied. The performance of these techniques depends however critically on the feature extraction step. Here, we propose deep learning using autoencoders as a feature extraction method and evaluate extensively the performance of multiple designs. The models presented are evaluated on publicly available synthetic and real “in vivo” datasets, with various numbers of clusters. The proposed methods indicate a higher performance for the process of spike sorting when compared to other state-of-the-art techniques.

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Section: State Of the Artmentioning

confidence: 99%

A study of autoencoders as a feature extraction technique for spike sorting

et al. 2023

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“…The reduction of dimensionality, including the Principal Component Analysis (PCA) or Independent Component Analysis (ICA) [21], is a task employed prior to clustering. In a recent article, Dimitriadis et al introduced the stochastic neighboring embedding (t-sne) dimensionality reduction method as a visualization tool in the spike sorting process.…”

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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Partially binarized neural networks for efficient spike sorting

Valencia

Alimohammad

2022

Biomed. Eng. Lett.

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ICA feature extraction for spike sorting of single-channel records

Cited by 6 publications

References 17 publications

A study of autoencoders as a feature extraction technique for spike sorting

A study of autoencoders as a feature extraction technique for spike sorting

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

Partially binarized neural networks for efficient spike sorting

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