A novel single-trial event-related potential estimation method based on compressed sensing  

作　　者：Zhihua Huang Minghong Li Shangchuan Yang Yuanye Ma Changle Zhou 

机构地区：[1]College of Mathematics and Computer Science,Fuzhou University [2]Kunming Institute of Zoology,CAS [3]Cognitive Science Department,Xiamen University

出　　处：《Neuroscience Bulletin》2013年第6期788-797,共10页神经科学通报（英文版）

基　　金：supported by National Basic Research Development Program (973 program) of China (2012CB825500,2011CB707800);National Natural Science Foundation of China (31271168);Natural Science Foundation of Fujian Province, China (2011J01344)

摘　　要：Cognitive functions are often studied using eventrelated potentials（ERPs）that are usually estimated by an averaging algorithm.Clearly,estimation of single-trial ERPs can provide researchers with many more details of cognitive activity than the averaging algorithm.A novel method to estimate single-trial ERPs is proposed in this paper.This method includes two key ideas.First,singular value decomposition was used to construct a matrix,which mapped singletrial electroencephalographic recordings（EEG）into a low-dimensional vector that contained little information from the spontaneous EEG.Second,we used the theory of compressed sensing to build a procedure to restore single-trial ERPs from this low-dimensional vector.ERPs are sparse or approximately sparse in the frequency domain.This fact allowed us to use the theory of compressed sensing.We verified this method in simulated and real data.Our method and dVCA（differentially variable component analysis）,another method of single-trial ERPs estimation,were both used to estimate single-trial ERPs from the same simulated data.Results demonstrated that our method significantly outperforms dVCA under various conditions of signal-to-noise ratio.Moreover,the single-trial ERPs estimated from the real data by our method are statistically consistent with the theories of cognitive science.Cognitive functions are often studied using eventrelated potentials（ERPs）that are usually estimated by an averaging algorithm.Clearly,estimation of single-trial ERPs can provide researchers with many more details of cognitive activity than the averaging algorithm.A novel method to estimate single-trial ERPs is proposed in this paper.This method includes two key ideas.First,singular value decomposition was used to construct a matrix,which mapped singletrial electroencephalographic recordings（EEG）into a low-dimensional vector that contained little information from the spontaneous EEG.Second,we used the theory of compressed sensing to build a procedure to restore single-trial ERPs from this low-dimensional vector.ERPs are sparse or approximately sparse in the frequency domain.This fact allowed us to use the theory of compressed sensing.We verified this method in simulated and real data.Our method and dVCA（differentially variable component analysis）,another method of single-trial ERPs estimation,were both used to estimate single-trial ERPs from the same simulated data.Results demonstrated that our method significantly outperforms dVCA under various conditions of signal-to-noise ratio.Moreover,the single-trial ERPs estimated from the real data by our method are statistically consistent with the theories of cognitive science.

关 键 词：compressed sensing event-related potentials single-trial electroencephalography singular value decomposition 

分 类 号：TP391.41[自动化与计算机技术—计算机应用技术]