Model selection in spatio-temporal electromagnetic source analysis

被引：10

作者：

Waldorp, LJ

Huizenga, HM

Nehorai, A

Grasman, RPPP

Molenaar, PCM

机构：

[1] Univ Amsterdam, Dept Psychol, NL-1018 WB Amsterdam, Netherlands

[2] Univ Illinois, Dept Elect & Comp Engn, Chicago, IL 60607 USA

来源：

IEEE TRANSACTIONS ON BIOMEDICAL ENGINEERING | 2005年 / 52卷 / 03期

关键词：

Akaike criterion; Bayesian criterion; EEG; MEG; model order selection; source localization; testing source activity;

D O I：

10.1109/TBME.2004.842982

中图分类号：

R318 [生物医学工程];

学科分类号：

0831 ;

摘要：

Several methods [model selection procedures (MSPs)] to determine the number of sources in electroencephalogram (EEG) and magnetoencphalogram (MEG) data have previously been investigated in an instantaneous analysis. In this paper, these MSPs are extended to a spatio-temporal analysis if possible. It is seen that the residual variance (RV) tends to overestimate the number of sources. The Akaike information criterion (AIC) and the Wald test on amplitudes (WA) and the Wald test on locations (WL) have the highest probabilities of selecting the correct number of sources. The WA has the advantage that it offers the opportunity to test which source is active at which time sample.

引用

页码：414 / 420

页数：7

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