基于回声状态网络的脑电信号特征提取

Extraction of the EEG Signal Feature Based on Echo State Networks

在脑电(EEG)信号自动检测和分类的研究中,EEG信号的特征提取至关重要。本文分析了目前主要EEG信号特征提取方法的优缺点,并提出了一种基于回声状态网络(ESN)的EEG信号特征提取方法。该方法可以实现EEG信号的非线性特征提取,并且其特征提取过程是近似可逆的,因而在特征提取过程中损失的信息较少。该方法在EEG信号特征提取过程中,主要计算量是求解状态矩阵的伪逆,计算简单高效。在对波恩大学癫痫研究所的EEG数据库进行多类别分类的实验中,本文所提出的EEG信号特征提取方法展现出了良好的性能。

The performance of an electroencephalography（EEG） automatic detection and classification system mainly depends on the feature extraction of EEG signal.This paper analyses the advantages and disadvantages of the current EEG feature extraction methods,and then presents a new EEG feature extraction method based on echo state networks（ESN）.The new method is a nonlinear method,and can extract the EEG features reversibly.Therefore,the information lost in the process of feature extraction is much less than that of the traditional EEG.Additionally,the realization of this method just needs to compute the pseudo inverse of a matrix,which keeps it efficient.Experimental results have showed that the new method could well accomplish the task of automatic detection and classification of EEG signals.