Sepulveda Francisco
Brain-Computer Interfaces Group, Department of Computing and Electronic Systems, University of Essex, Wivenhoe Park, Colchester, Essex CO4 3SQ, United Kingdom.
Int Rev Neurobiol. 2009;86:93-106. doi: 10.1016/S0074-7742(09)86007-8.
Research in BMIs has grown rapidly in the last few years. However, little attention has been paid to the overall system behavior, most published work being focused on the signal classification (i.e., translation) stage. More recently an increasing amount of work has been centred around the feature selection stage that precedes translation. The emphasis in feature selection and translation has stemmed from the large number of researchers with a machine learning or pattern recognition background who have recently joined the field. While there is an important contribution to BMIs, two crucial elements have not been sufficiently explored: the selection of suitable mental tasks and feedback protocols. This review presents an overview of BMIs and its main elements, with a focus on why each stage is important for the overall performance of such systems.
在过去几年中,脑机接口(BMI)的研究发展迅速。然而,人们很少关注整个系统的行为,大多数已发表的工作都集中在信号分类(即转换)阶段。最近,越来越多的工作围绕着转换之前的特征选择阶段展开。特征选择和转换方面的重点源于大量具有机器学习或模式识别背景的研究人员最近加入了该领域。虽然这对脑机接口有重要贡献,但有两个关键要素尚未得到充分探索:合适心理任务的选择和反馈协议。本综述概述了脑机接口及其主要要素,重点关注每个阶段为何对这类系统的整体性能很重要。