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BCILAB: a platform for brain-computer interface development

机译:BCILAB:脑机接口开发平台

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摘要

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.
机译:在过去的二十年中,我们在建模脑电图,功能近红外光谱等记录的脑信号以及为各种目的获取用户认知状态,反应或意图的实时估计的能力方面取得了巨大进步:恢复严重残疾者的交流,实现大脑驱动的控制,最近又加强了人机交互。继续这些进步(很大程度上是通过提高计算能力和方法来实现的),需要软件工具来简化新数据分析方法的创建,测试,评估和部署。方法。在这里,我们介绍BCILAB,这是一个基于MATLAB的开源工具箱,旨在通过提供100多种预先实现的方法和方法变体的有组织集合来满足开发和测试脑机接口(BCI)方法的需求,用于快速建立新方法原型的可扩展框架,以及用于系统测试和评估新实现的高度自动化的框架。主要结果。为了验证和说明该框架的使用,我们对来自最近BCI竞赛和快速连续视觉呈现任务的公开数据集进行了两个样本分析。我们证明了直接使用BCILAB可获得与当前BCI文献兼容的结果。意义。 BCILAB工具箱的目的是为BCI社区提供一种功能强大的工具箱,用于方法研究和评估,从而有助于加快该领域的创新步伐,同时补充了用于实时BCI实验,部署和使用的现有工具范围。

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  • 来源
    《Journal of neural engineering》 |2013年第5期|056014.1-056014.17|共17页
  • 作者单位

    Swartz Center for Computational Neuroscience, Institute for Neural Computation, University of California San Diego, 9500 Gilman Drive, La Jolla, CA 92093, USA;

    Swartz Center for Computational Neuroscience, Institute for Neural Computation, University of California San Diego, 9500 Gilman Drive, La Jolla, CA 92093, USA;

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