Wyrm: A Brain-Computer Interface Toolbox in Python

被引:0
作者
Bastian Venthur
Sven Dähne
Johannes Höhne
Hendrik Heller
Benjamin Blankertz
机构
[1] Technische Universität Berlin,Department of Neurotechnology
[2] Technische Universität,Department of Machine Learning
[3] Bernstein Center for Computational Neuroscience,undefined
来源
Neuroinformatics | 2015年 / 13卷
关键词
Brain-computer interface; BCI; EEG; ECoG; Toolbox; Python; Machine learning; Signal processing;
D O I
暂无
中图分类号
学科分类号
摘要
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.
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页码:471 / 486
页数:15
相关论文
共 96 条
[1]  
Blankertz B(2006)The BCI competition III: Validating alternative approachs to actual BCI problems IEEE Transactions on Neural Systems and Rehabilitation Engineering 14 153-159
[2]  
Müller KR(2008)Optimizing spatial filters for robust EEG single-trial analysis IEEE Signal Processing Magazine 25 41-56
[3]  
Krusienski D(2011)Single-trial analysis and classification of ERP components – a tutorial NeuroImage 56 814-825
[4]  
Schalk G(2010)Does the ”P300” speller depend on eye gaze? Journal of neural engineering 7 056,013-122
[5]  
Wolpaw JR(2014)SPoC: a novel framework for relating the amplitude of neuronal oscillations to behaviorally relevant parameters NeuroImage 86 111-179
[6]  
Schlögl A(2000)The mental prosthesis: assessing the speed of a p300-based brain-computer interface IEEE Transactions on Rehabilitation Engineering 8 174-110
[7]  
Pfurtscheller G(2014)On the interpretation of weight vectors of linear models in multivariate neuroimaging NeuroImage 87 96-95
[8]  
del R Millán J(2007)Matplotlib: A 2d graphics environment Computing in Science & amp; Engineering 9 90-439
[9]  
Schröder M(2013)BCILAB: a platform for brain–computer interface development Journal of neural engineering 10 056,014-20
[10]  
Birbaumer N(1982)Number of faults per line of code IEEE Transactions on SE– Software Engineering 8 437-2830