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BCILAB: a platform for brain-computer interface development

delete2013-08-28
delete291
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OA
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C
Christian Kothe *
S
Scott Makeig
DOI:10.1088/1741-2560/10/5/056014delete
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Abstract

Abstract

En 中文
Objective. The past two decades have seen dramatic progress in our ability to model brain signals recorded by electroencephalography, functional near-infrared spectroscopy, etc., and to derive real-time estimates of user cognitive state, response, or intent for a variety of purposes: to restore communication by the severely disabled, to effect brain-actuated control and, more recently, to augment human-computer interaction. Continuing these advances, largely achieved through increases in computational power and methods, requires software tools to streamline the creation, testing, evaluation and deployment of new data analysis methods. Approach. Here we present BCILAB, an open-source MATLAB-based toolbox built to address the need for the development and testing of brain-computer interface (BCI) methods by providing an organized collection of over 100 pre-implemented methods and method variants, an easily extensible framework for the rapid prototyping of new methods, and a highly automated framework for systematic testing and evaluation of new implementations. Main results. To validate and illustrate the use of the framework, we present two sample analyses of publicly available data sets from recent BCI competitions and from a rapid serial visual presentation task. We demonstrate the straightforward use of BCILAB to obtain results compatible with the current BCI literature. Significance. The aim of the BCILAB toolbox is to provide the BCI community a powerful toolkit for methods research and evaluation, thereby helping to accelerate the pace of innovation in the field, while complementing the existing spectrum of tools for real-time BCI experimentation, deployment and use.
Keywords:
OPEN-SOURCE SOFTWARE
SINGLE-TRIAL EEG
MOTOR IMAGERY
SPECTRAL FILTERS
CLASSIFICATION
POTENTIALS
COMPONENTS
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Journal

Journal of Neural Engineering cover
Journal of Neural Engineering
IF:
3.8
Papers:
4.0K
Citations:
1.4W

Organization

University of California System cover
University of California System
Scholars:
37.5W
Papers: 33.7W
Citations: 6.6K