基于时频域分析的运动想象脑电信号分类

Classification of motor imagery electroencephalogram signals based on a time-frequency analysis

人的不同运动想象会产生不同的脑电信号。脑机接口系统就是利用这种不同的脑电信号,利用外部的连接和控制设备将不同的思维活动与不同的指令结合起来,实现人脑和外部设备的通信。为了从包含各种噪声的脑电信号中提取特征,国内外学者运用各种方法,通过多种途径,力图达到最优的信号分类模式。文章介绍了一种新的方法运用于运动想象脑电信号分类,该方法基于脑电信号的时频域分析,结合C3,C4电极脑电信号间的相互关系,依据Fisher距离进行特征抽取,运用线性分类器进行分类。该算法运用到3名受试者的脑电数据中,分别对选取脑电信号特征频率段、Kappa值、和脑电信号特征选取不同时间段进行分析。分类效果因受试者而异,从65.0%到93.1%。

Human motor imagery tasks evoke electroencephalogram （EEG） signal changes. A brain computer interface is a system that can translate the electrical activity of brain for using in communication and control. To distinguish signals of interest from the background activity various feature extraction methods have been applied, we describe a new technique for the classification of motor imagery EEG recordings. The technique is based on a time-frequency analysis of EEG signals, regarding the relations between the EEG data obtained from the C3/C4 electrodes; the features were reduced according the Fisher distance This reduced feature set is finally fed to a linear discriminant for classification. The algorithm was applied to 3 subjects, and analyzed the different frequency band, Kappa number and time period of EEG. The classification performance of the proposed algorithm varied between 65% and 93.1% across subjects.