Liao Xiang, Yao Dezhong, Wu Dan, Li Chaoyi
Center of Neuroinformatics, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu 610054, China.
IEEE Trans Biomed Eng. 2007 May;54(5):821-31. doi: 10.1109/TBME.2006.889206.
Brain-computer interface (BCI) is to provide a communication channel that translates human intention reflected by a brain signal such as electroencephalogram (EEG) into a control signal for an output device. In recent years, the event-related desynchronization (ERD) and movement-related potentials (MRPs) are utilized as important features in motor related BCI system, and the common spatial patterns (CSP) algorithm has shown to be very useful for ERD-based classification. However, as MRPs are slow nonoscillatory EEG potential shifts, CSP is not an appropriate approach for MRPs-based classification. Here, another spatial filtering algorithm, discriminative spatial patterns (DSP), is newly introduced for better extraction of the difference in the amplitudes of MRPs, and it is integrated with CSP to extract the features from the EEG signals recorded during voluntary left versus right finger movement tasks. A support vector machines (SVM) based framework is designed as the classifier for the features. The results show that, for MRPs and ERD features, the combined spatial filters can realize the single-trial EEG classification better than anyone of DSP and CSP alone does. Thus, we propose an EEG-based BCI system with the two feature sets, one based on CSP (ERD) and the other based on DSP (MRPs), classified by SVM.
脑机接口（BCI）旨在提供一种通信通道，将诸如脑电图（EEG）等脑信号所反映的人类意图转化为输出设备的控制信号。近年来，事件相关去同步化（ERD）和运动相关电位（MRP）被用作运动相关BCI系统的重要特征，并且共同空间模式（CSP）算法已被证明对基于ERD的分类非常有用。然而，由于MRP是缓慢的非振荡性脑电电位偏移，CSP并不是基于MRP分类的合适方法。在此，新引入了另一种空间滤波算法——判别空间模式（DSP），以更好地提取MRP幅度差异，并且将其与CSP集成，以从自愿进行左、右手手指运动任务期间记录的脑电信号中提取特征。设计了一个基于支持向量机（SVM）的框架作为特征分类器。结果表明，对于MRP和ERD特征，组合空间滤波器比单独的DSP和CSP中的任何一个都能更好地实现单次试验脑电分类。因此，我们提出了一种基于脑电的BCI系统，该系统具有两个特征集，一个基于CSP（ERD），另一个基于DSP（MRP），由SVM进行分类。
