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Wyrm: A Brain-Computer Interface Toolbox in Python.

Overview of attention for article published in Neuroinformatics, May 2015
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About this Attention Score

  • Above-average Attention Score compared to outputs of the same age (60th percentile)

Mentioned by

4 tweeters

Readers on

61 Mendeley
Wyrm: A Brain-Computer Interface Toolbox in Python.
Published in
Neuroinformatics, May 2015
DOI 10.1007/s12021-015-9271-8
Pubmed ID

Venthur, Bastian, Dähne, Sven, Höhne, Johannes, Heller, Hendrik, Blankertz, Benjamin


In the last years Python has gained more and more traction in the scientific community. Projects like NumPy, SciPy, and Matplotlib have created a strong foundation for scientific computing in Python and machine learning packages like scikit-learn or packages for data analysis like Pandas are building on top of it. In this paper we present Wyrm ( https://github.com/bbci/wyrm ), an open source BCI toolbox in Python. Wyrm is applicable to a broad range of neuroscientific problems. It can be used as a toolbox for analysis and visualization of neurophysiological data and in real-time settings, like an online BCI application. In order to prevent software defects, Wyrm makes extensive use of unit testing. We will explain the key aspects of Wyrm's software architecture and design decisions for its data structure, and demonstrate and validate the use of our toolbox by presenting our approach to the classification tasks of two different data sets from the BCI Competition III. Furthermore, we will give a brief analysis of the data sets using our toolbox, and demonstrate how we implemented an online experiment using Wyrm. With Wyrm we add the final piece to our ongoing effort to provide a complete, free and open source BCI system in Python.

Twitter Demographics

The data shown below were collected from the profiles of 4 tweeters who shared this research output. Click here to find out more about how the information was compiled.

Mendeley readers

The data shown below were compiled from readership statistics for 61 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Indonesia 1 2%
Portugal 1 2%
Brazil 1 2%
Japan 1 2%
United States 1 2%
Unknown 56 92%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 21 34%
Student > Master 10 16%
Student > Bachelor 7 11%
Researcher 7 11%
Professor > Associate Professor 5 8%
Other 11 18%
Readers by discipline Count As %
Computer Science 22 36%
Engineering 16 26%
Neuroscience 9 15%
Biochemistry, Genetics and Molecular Biology 4 7%
Arts and Humanities 2 3%
Other 8 13%

Attention Score in Context

This research output has an Altmetric Attention Score of 3. This is our high-level measure of the quality and quantity of online attention that it has received. This Attention Score, as well as the ranking and number of research outputs shown below, was calculated when the research output was last mentioned on 05 June 2015.
All research outputs
of 5,190,641 outputs
Outputs from Neuroinformatics
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Outputs of similar age
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Outputs of similar age from Neuroinformatics
of 3 outputs
Altmetric has tracked 5,190,641 research outputs across all sources so far. This one has received more attention than most of these and is in the 60th percentile.
So far Altmetric has tracked 107 research outputs from this source. They receive a mean Attention Score of 3.2. This one has gotten more attention than average, scoring higher than 50% of its peers.
Older research outputs will score higher simply because they've had more time to accumulate mentions. To account for age we can compare this Altmetric Attention Score to the 173,672 tracked outputs that were published within six weeks on either side of this one in any source. This one has gotten more attention than average, scoring higher than 60% of its contemporaries.
We're also able to compare this research output to 3 others from the same source and published within six weeks on either side of this one. This one has scored higher than all of them