TY - GEN
T1 - EEG/MEG artifact suppression for improved neural activity estimation
AU - Maurer, Alexander
AU - Miao, Lifeng
AU - Zhang, Jun Jason
AU - Kovvali, Narayan
AU - Papandreou-Suppappola, Antonia
AU - Chakrabarti, Chaitali
PY - 2012/12/1
Y1 - 2012/12/1
N2 - 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.
AB - 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.
UR - http://www.scopus.com/inward/record.url?scp=84876223001&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=84876223001&partnerID=8YFLogxK
U2 - 10.1109/ACSSC.2012.6489311
DO - 10.1109/ACSSC.2012.6489311
M3 - Conference contribution
AN - SCOPUS:84876223001
SN - 9781467350518
T3 - Conference Record - Asilomar Conference on Signals, Systems and Computers
SP - 1646
EP - 1650
BT - Conference Record of the 46th Asilomar Conference on Signals, Systems and Computers, ASILOMAR 2012
T2 - 46th Asilomar Conference on Signals, Systems and Computers, ASILOMAR 2012
Y2 - 4 November 2012 through 7 November 2012
ER -