Determining the number of sources in high-density EEG recordings of event-related potentials by model order selection

Fengyu Cong, Zhaoshui He, Jarmo Hämäläinen, Andrzej Cichocki, Tapani Ristaniemi

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

11 Citations (Scopus)

Abstract

To high-density electroencephalography (EEG) recordings, determining the number of sources to separate the signal and the noise subspace is very important. A mostly used criterion is that percentage of variance of raw data explained by the selected principal components composing the signal space should be over 90%. Recently, a model order selection method named as GAP has been proposed. We investigated the two methods by performing independent component analysis (ICA) on the estimated signal subspace, assuming the number of selected principal components composing the signal subspace is equal to the number of sources of brain activities. Through examining wavelet-filtered EEG recordings (128 electrodes) of ERPs, ICA with the reference to GAP decomposed 14 selected principal components reliably into 14 independent components, and ICA decomposition with the variance explained method was not reliable, indicating that the number of sources, as well as the signal subspace, should be well estimated through GAP.

Original languageEnglish
Title of host publication2011 IEEE International Workshop on Machine Learning for Signal Processing - Proceedings of MLSP 2011
DOIs
Publication statusPublished - 2011
Externally publishedYes
Event21st IEEE International Workshop on Machine Learning for Signal Processing, MLSP 2011 - Beijing, China
Duration: 18 Sep 201121 Sep 2011

Publication series

NameIEEE International Workshop on Machine Learning for Signal Processing

Conference

Conference21st IEEE International Workshop on Machine Learning for Signal Processing, MLSP 2011
Country/TerritoryChina
CityBeijing
Period18/09/1121/09/11

Keywords

  • Event-related potential
  • independent/principal component analysis
  • model order selection
  • number of sources
  • reliability
  • wavelet filter

Fingerprint

Dive into the research topics of 'Determining the number of sources in high-density EEG recordings of event-related potentials by model order selection'. Together they form a unique fingerprint.

Cite this