基于邻域空间模式的运动相关电位特征提取方法

为解决脑-机接口(BCI)研究中所采集的脑电图(EEG)信号数据分布复杂和训练样本不足的问题,文中提出了一种新的特征提取方法--邻域空间模式(NSP)算法,用于提取BCI想象肢体运动分类算法中使用的重要分类特征--运动相关电位(MRPs).NSP算法不需要对样本的数据分布进行假设,主要利用样本的邻域关系和类别信息寻找最佳投影方向,使得映射后邻域内异类样本距离之和与同类样本距离之和的比值最大化.采用BCI竞赛2003和2001的其中两组数据进行实验,结果表明NSP算法能更有效地提取MRPs特征.

An Approach to Extracting Features of Movement-Related Potentials Based on Neighborhood Spatial Pattern

In order to remedy the complex distribution of recorded electroencephalogram (EEG) data and the shortage of training data in terms of brain-computer interface (BCI), a novel approach named neighborhood spatial pattern (NSP) is proposed to extract movement-related potentials (MRPs), which constitute the most important features utilized in the classification algorithms for the motor-imagery-based BCI. NSP searches the optimal direction which maximizes the ratio of the between-class distance to the within-class distance of the neighborhood in the projected space. During the search, no assumptions about the latent data distribution should be made, and only the neighborhood relationship and the label information are required. NSP is also applied to two datasets from BCI competitions 2003 and 2001. The results show that NSP can effectively extract MPRs features.