MEG analysis using ICA with spatial arrangement

Shunta Echigoya, Satoshi Honda

Research output: Chapter in Book/Report/Conference proceedingConference contribution


One of the problems in analyzing magnetoencepharograpy (MEG) is that brain signals are contaminated with high-level noise and artifacts. Although independent component analysis (ICA) is a useful method to separate brain signals from other components, not all signals are statistically independent. Additionally, each component should be judged as a brain signals or the others objectively. In this paper, we propose two ICA approaches that utilize spatial characteristics of brain activities to separate signals more precisely and meaningfully. Numerical experiments showed that it is helpful for ICA to use spatial arrangement, and a experiment using auditory evoked field (AEF) data brought out the features of proposal techniques.

Original languageEnglish
Title of host publication2006 SICE-ICASE International Joint Conference
Number of pages6
Publication statusPublished - 2006 Dec 1
Event2006 SICE-ICASE International Joint Conference - Busan, Korea, Republic of
Duration: 2006 Oct 182006 Oct 21

Publication series

Name2006 SICE-ICASE International Joint Conference


Other2006 SICE-ICASE International Joint Conference
Country/TerritoryKorea, Republic of


  • Independent component analysis
  • Magnetoencephalography
  • Spatial characteristic

ASJC Scopus subject areas

  • Computer Science Applications
  • Control and Systems Engineering
  • Electrical and Electronic Engineering


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