Hammon Paul S, de Sa Virginia R
Department of Electrical and Computer Engineering, University of California at San Diego, La Jolla, CA 92093-0409, USA.
IEEE Trans Biomed Eng. 2007 Mar;54(3):518-25. doi: 10.1109/TBME.2006.888833.
A brain-computer interface (BCI) is a system which allows direct translation of brain states into actions, bypassing the usual muscular pathways. A BCI system works by extracting user brain signals, applying machine learning algorithms to classify the user's brain state, and performing a computer-controlled action. Our goal is to improve brain state classification. Perhaps the most obvious way to improve classification performance is the selection of an advanced learning algorithm. However, it is now well known in the BCI community that careful selection of preprocessing steps is crucial to the success of any classification scheme. Furthermore, recent work indicates that combining the output of multiple classifiers (meta-classification) leads to improved classification rates relative to single classifiers (Dornhege et al., 2004). In this paper, we develop an automated approach which systematically analyzes the relative contributions of different preprocessing and meta-classification approaches. We apply this procedure to three data sets drawn from BCI Competition 2003 (Blankertz et al., 2004) and BCI Competition III (Blankertz et al., 2006), each of which exhibit very different characteristics. Our final classification results compare favorably with those from past BCI competitions. Additionally, we analyze the relative contributions of individual preprocessing and meta-classification choices and discuss which types of BCI data benefit most from specific algorithms.
脑机接口（BCI）是一种允许将脑状态直接转化为动作的系统，绕过了通常的肌肉传导路径。BCI系统的工作方式是提取用户的脑信号，应用机器学习算法对用户的脑状态进行分类，并执行计算机控制的动作。我们的目标是提高脑状态分类的准确性。或许提高分类性能最明显的方法是选择先进的学习算法。然而，BCI领域目前已经清楚地认识到，仔细选择预处理步骤对于任何分类方案的成功都至关重要。此外，最近的研究表明，相对于单分类器而言，组合多个分类器的输出（元分类）能够提高分类准确率（多恩赫格等人，2004年）。在本文中，我们开发了一种自动化方法，系统地分析不同预处理和元分类方法的相对贡献。我们将此程序应用于从2003年BCI竞赛（布兰克茨等人，2004年）和第三届BCI竞赛（布兰克茨等人，2006年）中获取的三个数据集，每个数据集都具有非常不同的特征。我们最终的分类结果优于过去BCI竞赛的结果。此外，我们分析了各个预处理和元分类选择的相对贡献，并讨论了哪些类型的BCI数据从特定算法中受益最大。
