Dataset Information

SANTIA: a Matlab-based open-source toolbox for artifact detection and removal from extracellular neuronal signals.

ABSTRACT: Neuronal signals generally represent activation of the neuronal networks and give insights into brain functionalities. They are considered as fingerprints of actions and their processing across different structures of the brain. These recordings generate a large volume of data that are susceptible to noise and artifacts. Therefore, the review of these data to ensure high quality by automatically detecting and removing the artifacts is imperative. Toward this aim, this work proposes a custom-developed automatic artifact removal toolbox named, SANTIA (SigMate Advanced: a Novel Tool for Identification of Artifacts in Neuronal Signals). Developed in Matlab, SANTIA is an open-source toolbox that applies neural network-based machine learning techniques to label and train models to detect artifacts from the invasive neuronal signals known as local field potentials.

SUBMITTER: Fabietti M

PROVIDER: S-EPMC8292498 | biostudies-literature | 2021 Jul

REPOSITORIES: biostudies-literature

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SANTIA: a Matlab-based open-source toolbox for artifact detection and removal from extracellular neuronal signals.

Fabietti Marcos M Mahmud Mufti M Lotfi Ahmad A Kaiser M Shamim MS Averna Alberto A Guggenmos David J DJ Nudo Randolph J RJ Chiappalone Michela M Chen Jianhui J

Brain informatics 20210720 1

Neuronal signals generally represent activation of the neuronal networks and give insights into brain functionalities. They are considered as fingerprints of actions and their processing across different structures of the brain. These recordings generate a large volume of data that are susceptible to noise and artifacts. Therefore, the review of these data to ensure high quality by automatically detecting and removing the artifacts is imperative. Toward this aim, this work proposes a custom-deve ...[more]

PMID: 34283328

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Project description:MotivationAdvances in microscopy technology have led to the creation of high-throughput microscopes that are capable of generating several hundred gigabytes of images in a few days. Analyzing such wealth of data manually is nearly impossible and requires an automated approach. There are at present a number of open-source and commercial software packages that allow the user to apply algorithms of different degrees of sophistication to the images and extract desired metrics. However, the types of metrics that can be extracted are severely limited by the specific image processing algorithms that the application implements, and by the expertise of the user. In most commercial software, code unavailability prevents implementation by the end user of newly developed algorithms better suited for a particular type of imaging assay. While it is possible to implement new algorithms in open-source software, rewiring an image processing application requires a high degree of expertise. To obviate these limitations, we have developed an open-source high-throughput application that allows implementation of different biological assays such as cell tracking or ancestry recording, through the use of small, relatively simple image processing modules connected into sophisticated imaging pipelines. By connecting modules, non-expert users can apply the particular combination of well-established and novel algorithms developed by us and others that are best suited for each individual assay type. In addition, our data exploration and visualization modules make it easy to discover or select specific cell phenotypes from a heterogeneous population.AvailabilityCellAnimation is distributed under the Creative Commons Attribution-NonCommercial 3.0 Unported license (http://creativecommons.org/licenses/by-nc/3.0/). CellAnimationsource code and documentation may be downloaded from www.vanderbilt.edu/viibre/software/documents/CellAnimation.zip. Sample data are available at www.vanderbilt.edu/viibre/software/documents/movies.zip.Contactwalter.georgescu@vanderbilt.eduSupplementary informationSupplementary data available at Bioinformatics online.

Dataset Information

SANTIA: a Matlab-based open-source toolbox for artifact detection and removal from extracellular neuronal signals.

Publications

SANTIA: a Matlab-based open-source toolbox for artifact detection and removal from extracellular neuronal signals.

Similar Datasets

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