针对快速序列视觉呈现脑电信号的时空混合特征提取方法

A spatial-temporal hybrid feature extraction method for rapid serial visual presentation of electroencephalogram signals

快速序列视觉呈现脑机接口(RSVP-BCI)是目前基于人脑对目标进行早期发现任务中最为常用的技术,该技术能够获取人脑对环境的快速感知。脑电信号(EEG)具有非平稳和信噪比较低的特点,因此在单次试验中准确解码大脑活动较为困难。为解决RSVP-BCI技术中单次试验分类准确率不高的问题,本文提出一种基于时空域混合特征提取的新方法。该方法充分考虑大脑活动的时空模式,分别在时域和空域采用主成分分析(PCA)和共空间模式(CSP)对EEG信号进行特征提取,构成时空混合CSP-PCA(STHCP)方法,通过时域、空域两次特征提取最大化目标类与非目标类之间的判别距离,有效地降低特征维数。STHCP的单试次解码曲线下面积(AUC)较三种基准算法[空间加权费希尔(Fisher)线性判决-PCA(SWFP)、CSP及PCA算法]分别提高了17.9%、22.2%及29.2%,为利用RSVP-BCI技术进行快速高效的目标检测提供了新方法。

Rapid serial visual presentation-brain computer interface(RSVP-BCI)is the most popular technology in the early discover task based on human brain.This algorithm can obtain the rapid perception of the environment by human brain.Decoding brain state based on single-trial of multichannel electroencephalogram(EEG)recording remains a challenge due to the low signal-to-noise ratio(SNR)and nonstationary.To solve the problem of low classification accuracy of single-trial in RSVP-BCI,this paper presents a new feature extraction algorithm which uses principal component analysis(PCA)and common spatial pattern(CSP)algorithm separately in spatial domain and time domain,creating a spatial-temporal hybrid CSP-PCA(STHCP)algorithm.By maximizing the discrimination distance between target and non-target,the feature dimensionality was reduced effectively.The area under the curve(AUC)of STHCP algorithm is higher than that of the three benchmark algorithms(SWFP,CSP and PCA)by 17.9%,22.2%and 29.2%,respectively.STHCP algorithm provides a new method for target detection.