Swartz Center for Computational Neuroscience, Institute for Neural Computation, University of California San Diego, 9500 Gilman Drive, La Jolla, CA 92093, USA.
J Neural Eng. 2013 Oct;10(5):056014. doi: 10.1088/1741-2560/10/5/056014. Epub 2013 Aug 28.
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.
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.
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.
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.
在过去的二十年中，我们在对脑电图、功能近红外光谱等记录的脑信号进行建模，并实时估计用户的认知状态、反应或意图方面取得了巨大进展，这些进展的应用目的多种多样：为严重残疾者恢复交流能力、实现大脑驱动控制，以及最近的增强人机交互能力。通过增加计算能力和方法，继续推进这些进展，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。
在这里，我们提出了 BCILAB，这是一个基于 MATLAB 的开源工具箱，旨在通过提供超过 100 种预先实现的方法和方法变体的组织集合、用于快速原型设计新方法的易于扩展的框架以及用于新实现的系统测试和评估的高度自动化框架，来满足脑机接口 (BCI) 方法的开发和测试需求。
为了验证和说明该框架的使用，我们对两个公开可用的数据集进行了分析，一个来自最近的 BCI 竞赛，另一个来自快速序列视觉呈现任务。我们展示了使用 BCILAB 获得与当前 BCI 文献兼容的结果的简单方法。
BCILAB 工具包的目的是为 BCI 社区提供强大的方法研究和评估工具包，从而帮助加速该领域的创新步伐，同时补充实时 BCI 实验、部署和使用的现有工具集。
