NeoAnalysis: a Python-based toolbox for quick electrophysiological data processing and analysis.
Background: In a typical electrophysiological experiment, especially one that includes studying animal behavior, the data collected normally contain spikes, local field potentials, behavioral responses and other associated data. In order to obtain informative results, the data must be analyzed simul...
| Publicado en: | BioMedical Engineering OnLine Vol. 16; pp. 1 - 18 |
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| Autores principales: | , , , , , |
| Formato: | pictorial tables/charts Journal Article |
| Publicado: |
BioMed Central
11/13/2017
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| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=126239748&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 126239748 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 1475925X 1CGX jtl: BioMedical Engineering OnLine issn: 1475925X maglogo: N pubinfo: dt: 11/13/2017 vid: 16 pid: 24147 pub: BioMed Central artinfo: ui: 126239748 126239748 NLM29132360 126239748 10.1186/s12938-017-0419-7 NLM29132360 126239748 ppf: 1 ppct: 17 formats: tig: atl: NeoAnalysis: a Python-based toolbox for quick electrophysiological data processing and analysis. aug: au: Bo Zhang Ji Dai Tao Zhang Zhang, Bo Dai, Ji Zhang, Tao affil: State Key Laboratory of Brain and Cognitive Sciences, Institute of Psychology, Chinese Academy of Sciences, Beijing 100101, China sug: subj: Software Signal Processing, Computer Assisted Electrophysiology Saccades Physiology Algorithms User-Computer Interface Funding Source ab: Background: In a typical electrophysiological experiment, especially one that includes studying animal behavior, the data collected normally contain spikes, local field potentials, behavioral responses and other associated data. In order to obtain informative results, the data must be analyzed simultaneously with the experimental settings. However, most open-source toolboxes currently available for data analysis were developed to handle only a portion of the data and did not take into account the sorting of experimental conditions. Additionally, these toolboxes require that the input data be in a specific format, which can be inconvenient to users. Therefore, the development of a highly integrated toolbox that can process multiple types of data regardless of input data format and perform basic analysis for general electrophysiological experiments is incredibly useful.Results: Here, we report the development of a Python based open-source toolbox, referred to as NeoAnalysis, to be used for quick electrophysiological data processing and analysis. The toolbox can import data from different data acquisition systems regardless of their formats and automatically combine different types of data into a single file with a standardized format. In cases where additional spike sorting is needed, NeoAnalysis provides a module to perform efficient offline sorting with a user-friendly interface. Then, NeoAnalysis can perform regular analog signal processing, spike train, and local field potentials analysis, behavioral response (e.g. saccade) detection and extraction, with several options available for data plotting and statistics. Particularly, it can automatically generate sorted results without requiring users to manually sort data beforehand. In addition, NeoAnalysis can organize all of the relevant data into an informative table on a trial-by-trial basis for data visualization. Finally, NeoAnalysis supports analysis at the population level.Conclusions: With the multitude of general-purpose functions provided by NeoAnalysis, users can easily obtain publication-quality figures without writing complex codes. NeoAnalysis is a powerful and valuable toolbox for users doing electrophysiological experiments. pubtype: Academic Journal doctype: pictorial tables/charts Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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