Abstract

Electroencephalography (EEG) and magnetoencephalography (MEG) measurements can be used to monitor neural activity, that is generally characterized using current or magnetic dipole source models with time-varying amplitude, position, and moment parameters. The EEG/MEG measurements, however, often contain artifacts that do not originate from the brain. These artifacts can include patient movement, normal heart electrical activity, muscle and eye movement, or equipment and environmental clutter. In this paper, we propose a novel neural activity estimation approach that integrates particle filtering with the probabilistic data association filter in order to validate neural measurements and suppress artifacts before estimating neural activity. Simulations using synthetic data with this approach demonstrate high performance in suppressing artifacts and tracking neural activity; results for real data are also presented.

Original languageEnglish (US)
Title of host publicationConference Record of the 46th Asilomar Conference on Signals, Systems and Computers, ASILOMAR 2012
Pages1646-1650
Number of pages5
DOIs
StatePublished - Dec 1 2012
Event46th Asilomar Conference on Signals, Systems and Computers, ASILOMAR 2012 - Pacific Grove, CA, United States
Duration: Nov 4 2012Nov 7 2012

Publication series

NameConference Record - Asilomar Conference on Signals, Systems and Computers
ISSN (Print)1058-6393

Other

Other46th Asilomar Conference on Signals, Systems and Computers, ASILOMAR 2012
Country/TerritoryUnited States
CityPacific Grove, CA
Period11/4/1211/7/12

ASJC Scopus subject areas

  • Signal Processing
  • Computer Networks and Communications

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