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STEN 1.0: Statistical Toolbox for Electrical Neuroimaging

Knebel,Jean-François; Notter, Michael Philipp

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  <identifier identifierType="DOI">10.5281/zenodo.1167723</identifier>
      <nameIdentifier nameIdentifierScheme="ORCID" schemeURI="">0000-0003-1665-1724</nameIdentifier>
      <creatorName>Notter, Michael Philipp</creatorName>
      <givenName>Michael Philipp</givenName>
      <nameIdentifier nameIdentifierScheme="ORCID" schemeURI="">0000-0002-5866-047X</nameIdentifier>
    <title>STEN 1.0: Statistical Toolbox for Electrical Neuroimaging</title>
    <date dateType="Issued">2012-01-24</date>
  <resourceType resourceTypeGeneral="Software"/>
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    <relatedIdentifier relatedIdentifierType="DOI" relationType="IsVersionOf">10.5281/zenodo.1164037</relatedIdentifier>
    <relatedIdentifier relatedIdentifierType="URL" relationType="IsPartOf"></relatedIdentifier>
    <rights rightsURI="info:eu-repo/semantics/restrictedAccess">Restricted Access</rights>
    <description descriptionType="Abstract">&lt;ul&gt;
	&lt;li&gt;What is STEN?
		&lt;li&gt;STEN is an open source software toolbox based on Python and R that can be used to compute statistics on several measures of electro- and magnetoencephalographic (EEG and MEG) signals.&lt;/li&gt;
		&lt;li&gt;STEN enables the sample-point and sensor-wise analysis of EEG and MEG data, but also at the level of Global Field Power (GFP) and distributed neural source estimations (e.g. LAURA or LORETTA) by means of parametric and non-parametric (bootstrapping) repeated measure ANOVAs, ANCOVAs and regression analyses. Correction thresholds for temporal and spatial auto-correlations in the data can be individually adjusted.&lt;br&gt;
	&lt;li&gt;How to install
		&lt;li&gt;First install&amp;nbsp;&lt;a href=""&gt;StenSetup1.0.exe&amp;nbsp;&lt;/a&gt;&amp;nbsp;once.&lt;/li&gt;
		&lt;li&gt;After install&amp;nbsp;&lt;a href=""&gt;StenUpdate_20150610.exe&amp;nbsp;&lt;/a&gt;&lt;br&gt;
	&lt;li&gt;Technical notes
		&lt;li&gt;The currently available STEN toolbox is a beta version. All statistical computations and their outcomes have been extensively validated. However, some minor bugs at the visualization level still need to be fixed.&lt;/li&gt;
		&lt;li&gt;The most commonly used input data for STEN so far are evoked potential files with header (.eph files), i.e. ASCII files (plain metrics files with header) that have been produced with our partner software CarTool ( STEN produces outputs in the .eph format.&lt;/li&gt;
		&lt;li&gt;STEN uses the Enthought distribution of Python (free only for academics) and the R software, relaese 2.11. The installation of STEN (and necessary software components) is realized via an easy-to-handle executable file (.exe). Up to date, STEN is developed for usage on Windows systems (in particular, XP and Windows 7)&lt;/li&gt;
	&lt;li&gt;How to cite STEN
		&lt;li&gt;It is required to cite STEN in subsequent publications, if it has been effectively used to process, or manipulate the data. Therefore, we ask to add the following sentence in the Acknowledgments section:&lt;/li&gt;
		&lt;li&gt;The STEN toolbox ( has been programmed by Jean-Fran&amp;ccedil;ois Knebel and Michael Notter, from the Laboratory for Investigative Neurophysiology (the LINE), Lausanne, Switzerland, and is supported by the Center for Biomedical Imaging (CIBM) of Geneva and Lausanne and by National Center of Competence in Research project &amp;ldquo;SYNAPSY &amp;ndash; The Synaptic Bases of Mental Disease&amp;rdquo;; project no. 51AU40_125759.&lt;/li&gt;
		&lt;li&gt;In addition, reference to STEN within the Material and Methods section should be done in the following way:&lt;/li&gt;
		&lt;li&gt;The analysis was performed using the STEN toolbox developed by Jean-Fran&amp;ccedil;ois Knebel and Michael Notter (;/li&gt;
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