| Title |
Wyrm: A Brain-Computer Interface Toolbox in Python
|
|---|---|
| Published in |
Neuroinformatics, May 2015
|
| DOI | 10.1007/s12021-015-9271-8 |
| Pubmed ID | |
| Authors |
Bastian Venthur, Sven Dähne, Johannes Höhne, Hendrik Heller, Benjamin Blankertz |
| Abstract |
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. |
X Demographics
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Geographical breakdown
| Country | Count | As % |
|---|---|---|
| United States | 1 | 50% |
| Italy | 1 | 50% |
Demographic breakdown
| Type | Count | As % |
|---|---|---|
| Members of the public | 1 | 50% |
| Practitioners (doctors, other healthcare professionals) | 1 | 50% |
Mendeley readers
The data shown below were compiled from readership statistics for 118 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Portugal | 1 | <1% |
| Indonesia | 1 | <1% |
| Brazil | 1 | <1% |
| Japan | 1 | <1% |
| United States | 1 | <1% |
| Unknown | 113 | 96% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Student > Ph. D. Student | 27 | 23% |
| Student > Master | 21 | 18% |
| Student > Bachelor | 17 | 14% |
| Researcher | 13 | 11% |
| Professor > Associate Professor | 7 | 6% |
| Other | 18 | 15% |
| Unknown | 15 | 13% |
| Readers by discipline | Count | As % |
|---|---|---|
| Engineering | 33 | 28% |
| Computer Science | 26 | 22% |
| Neuroscience | 14 | 12% |
| Psychology | 8 | 7% |
| Medicine and Dentistry | 6 | 5% |
| Other | 11 | 9% |
| Unknown | 20 | 17% |
Attention Score in Context
This research output has an Altmetric Attention Score of 2. 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.
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#14,717,488
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Outputs from Neuroinformatics
#220
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Outputs of similar age from Neuroinformatics
#2
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