结合子空间方法和提升小波变换提取事件相关脑电位

Efficient Extraction of Event Related Potentials by the Combination of Subspace Method and Lift Wavelet Transform

我们提出了一种提取事件相关脑电位的复合方法。它用奇异值分解方法将含噪信号分解为噪声子空间和信号子空间,将含噪信号正交投影到信号子空间进行初步除噪,随后将得到的信号进行提升小波变换,对变换结果进行一维小波重构进一步去除噪声,最后提取出ERP成分。介绍了基于奇异值分解的子空间方法和对信号进行提升小波去噪的实现方法。仿真结果表明,结合两种方法提取事件相关脑电信号时,比单独采用其中一种方法的效果要好,并可减少提取事件相关电位所需的实验次数。对实验数据的处理结果表明,该方法的实际处理效果良好。

A new approach is put forward for reducing the number of trials required for the extraction of the brain event related potentials （ERPs）. The approach is developed by combining both the subspace methods and lift wavelet transform. First,the signal subspace is estimated by applying the singular value decomposition （SVD） to an enhanced version of the raw data obtained by orthonormal projection of the raw data onto the estimated signal subspace. At the same time, the colored noise is whitened. Next, the ERPs are extracted by lift wavelet construction of the enhanced version. Simulation results show that combination of both the subspace methods provides much better capability than does each of them. The experiments showed that the practical results were good.