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

被引：2
|
作者：
Huang ZhiHua [1 ,2 ,3 ]
Li MingHong [4 ]
Zhou ChangLe [1 ,2 ]
Ma YuanYe [4 ]
机构：
[1] Xiamen Univ, Dept Cognit Sci, Xiamen 361005, Peoples R China
[2] Xiamen Univ, Fujian Key Lab Brain Like Intelligent Syst, Xiamen 361005, Peoples R China
[3] Fuzhou Univ, Coll Math & Comp Sci, Fuzhou 350108, Peoples R China
[4] Chinese Acad Sci, Kunming Inst Zool, Kunming 650223, Peoples R China
来源：
SCIENCE CHINA-LIFE SCIENCES | 2012年 / 55卷 / 01期
基金：
中国国家自然科学基金;
关键词：
classification; spatial-temporal signal model; optimization; logistic regression; SingleTrialEM; TO-NOISE RATIO; VARIABILITY; SIGNAL; ERPS; P50; INHIBITION; LATENCIES;
D O I：
10.1007/s11427-011-4263-x
中图分类号：
Q [生物科学];
学科分类号：
07 ; 0710 ; 09 ;
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.
引用
收藏
页码：57 / 67
页数：11
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