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Code for detecting and classifying epileptiform activity (EA)

Heining, Katharina


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<oai_dc:dc xmlns:dc="http://purl.org/dc/elements/1.1/" xmlns:oai_dc="http://www.openarchives.org/OAI/2.0/oai_dc/" xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance" xsi:schemaLocation="http://www.openarchives.org/OAI/2.0/oai_dc/ http://www.openarchives.org/OAI/2.0/oai_dc.xsd">
  <dc:creator>Heining, Katharina</dc:creator>
  <dc:date>2020-10-20</dc:date>
  <dc:description>Code for detecting and classifying epileptiform activity (EA)
Written by Katharina Heining, last modified 2020/10/20  
Institution: University of Freiburg, Germany
Accompanying Paschen et al. (2020), eLife

The subdirectory core contains the main code:  


	 ed_detection.py:    wrapper for preprocessing, spike detection and spike sorting  
	 artisfaction.py:    semiautomatic identification of artifacts
	 blipS.py:            spike detection* blipsort.py:        spike sorting
	 ea_analysis.py:        wrapper for burst detection and classification
	 somify.py:            projecting data on a SOM and SOM plotting
	 helpers.py:            supportive functions for the other scripts
	 ea_management.py:    reading data, handling results, recording-class functions


configAnalysis.yml contains parameters used for analyses  
som.h5 holds the SOM obtained from reference dataset -- see Heining et al. (2019), referenced below.*  

The subdirectory code_for_figures contains the code used for illustration (Supplementary Figure 1).
 
The code contained in this folder is © K. Heining, 2020, developed at the University of Freiburg.  
This code is made available under the BSD license enclosed with the software (see licence.txt).
Over and above the legal restrictions imposed by this license, if you use this software for an academic publication then you are obliged to provide proper attribution.
For this, you need to cite the paper that describes the code:  
* Heining, K., Kilias, A., Janz, P., Häussler, U., Kumar, A., Haas, C. A., and Egert, U.
(2019). Bursts with high and low load of epileptiform spikes show context-dependent
correlations in epileptic mice. eNeuro, 6(5).</dc:description>
  <dc:description>This work was supported by the German Research Foundation as part of the Cluster of Excellence 'BrainLinks-BrainTools' within the framework of the German Excellence Initiative (grant number EXC 1086) and through grant no INST 39/963-1 FUGG (bwForCluster NEMO), the State of Baden-Wuerttemberg through bwHPC, and by the Federal Ministry of Education and Research (BMBF, grant number FKZ 1GQ0830 and 16PGF0070), co-financed by the European Union/European Regional Development Fund (TIGER, A31).</dc:description>
  <dc:identifier>https://zenodo.org/record/4110614</dc:identifier>
  <dc:identifier>10.5281/zenodo.4110614</dc:identifier>
  <dc:identifier>oai:zenodo.org:4110614</dc:identifier>
  <dc:relation>doi:10.5281/zenodo.4110613</dc:relation>
  <dc:rights>info:eu-repo/semantics/openAccess</dc:rights>
  <dc:rights>https://opensource.org/licenses/MIT</dc:rights>
  <dc:subject>epileptiform activity</dc:subject>
  <dc:subject>local field potential</dc:subject>
  <dc:subject>detection of epileptiform spikes</dc:subject>
  <dc:title>Code for detecting and classifying epileptiform activity (EA)</dc:title>
  <dc:type>info:eu-repo/semantics/other</dc:type>
  <dc:type>software</dc:type>
</oai_dc:dc>
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