作　　者： ; ; ;

机构地区： 华南理工大学自动化科学与工程学院

出　　处： 《自动化与仪表》 2008年第4期1-4,共4页

摘　　要： 对基于左右手想象的脑机接口数据进行了离线分析。在预处理过程中,采用4阶椭圆带通滤波器进行8～12Hz带通滤波,提取Mu节律对应的EEG信号。选取时间为4～5.5s内的C3、C4通道的幅值和之比作为时域分类特征;在频域范围内,对滤波后数据进行10阶的AR模型功率谱估计作为频域特征;将时域特征和频域特征结合成时频特征向量作为分类特征;在分类器的选择上,使用线性感知器作为分类准则对特征向量进行分类训练。结果表明,经过滤波后的分类结果比未经过滤波的效果要好。选用C3、C4通道的时频特征向量作为分类特征,表达意义简单、明了,且能将某些时频的优点结合,分类准确率较高,且分类速度快,能满足实时性要求。 In this paper,outline analysis to BCI data which is based on motor imagination of left and right hand is finished. In the preprocessing,we use the 4-order band-pass elliptical filter to do 8-12Hz filtering in EEG. We extract the amplitude ratio of C3 and C4 in 4-5.5 second as time feature. In frequency extraction ,we use the 10-order AR model power spectral density as to be the frequency feature. And time-frequency feature combines the time feature and the frequency feature as the final feature vector. In this paper,we choose the perceptron algorithm as the classifier to classify the time-frequency feature vector. From the classified result it can be seen that the effection after filtering is better than before. Selecting the time-frequency feature vector of channel of C3, C4 as the classified feature makes it clearly and simply, and can combines the advantages of time feature and frequency feature, and the classification is great and fast, and can meet the need of timing.

关 键 词： 脑电信号 脑机接口 时频特征 带通滤波 线性感知器

领　　域： [自动化与计算机技术] [自动化与计算机技术]