A classification-based method to estimate event-related potentials from single trial EEG

作     者：HUANG ZhiHua LI MingHong ZHOU ChangLe MA YuanYe 

作者机构：Cognitive Science DepartmentXiamen UniversityXiamen 361005China Fujian Key Laboratory of the Brain-like Intelligent Systems(Xiamen University)Xiamen 361005China College of Mathematics and Computer ScienceFuzhou UniversityFuzhou 350108China Kunming Institute of ZoologyChinese Academy of SciencesKunming 650223China 

出 版 物：《Science China(Life Sciences)》 (中国科学（生命科学英文版）)

年 卷 期：2012年第55卷第1期

页      面：57-67页

核心收录：

学科分类：02[经济学] 0202[经济学-应用经济学] 020208[经济学-统计学] 07[理学] 081203[工学-计算机应用技术] 08[工学] 0714[理学-统计学（可授理学、经济学学位）] 0835[工学-软件工程] 070103[理学-概率论与数理统计] 0701[理学-数学] 0812[工学-计算机科学与技术（可授工学、理学学位）] 

基　　金：supported by the National Natural Science Foundation of China (Grant No. 30670669) National Basic Research Program of China (Grant No. 2007CB947703) Natural Science Foundation of Fujian Province (Grant No. 2011J01344) Science and Technology Development Foundation of Fuzhou University (Grant No. 2009-XQ-25) 

主　　题：classification spatial-temporal signal model optimization logistic regression SingleTrialEM 

摘      要：A novel method based on machine learning is developed to estimate event-related potentials from single trial electroencephalography. This paper builds a basic framework using classification and an optimization model based on this framework for estimating event-related potentials. Then the SingleTrialEM algorithm is derived by introducing a logistic regression model, which could be obtained by training before SingleTrialEM is used, to instantiate the optimization model. The simulation tests demonstrate that the proposed method is correct and solid. The advantage of this method is verified by the comparison between this method and the Woody filter in simulation tests. Also, the cognitive test results are consistent with the conclusions of cognitive science.