NDLI: Linear Dynamical System Response Modeling of Trial-to-Trial Amplitude Variability in Event-Related MEG/EEG

Content Provider	IEEE Xplore Digital Library
Author	Limpiti, Tulaya Van Veen, Barry D. Nagarajan, Srikantan S. Attias, Hagai T.
Copyright Year	2007
Description	Author affiliation: University of Wisconsin-Madison, Dept. of Electrical and Computer Engineering, Madison, WI (Limpiti, Tulaya; Van Veen, Barry D.) \|\| Golden Metallic, Inc., San Francisco, CA (Attias, Hagai T.) \|\| UC San Francisco, Biomagnetic Imaging Lab, Dept. of Radiology, San Francisco, CA; UCSF/UC Berkeley, Joint Graduate Group in Bioengineering, CA (Nagarajan, Srikantan S.)
Abstract	We propose a linear dynamical system response (LDSR) model to describe amplitude variability across trials in event-related magnetoencephalographic/electroencephalographic (MEG/EEG) data. Variability across trials may reflect habituation, fatigue, or changes in cognitive states of the brain. A wide range of trial-to-trial variability can be represented using the LDSR model, including the constant response (CR) model as a limiting case. The spatiotemporal signal waveform is assumed constant but unknown, and is represented using a linear combination of spatial and temporal basis functions. The background noise is assumed to be spatially correlated with unknown covariance matrix. We obtain the maximum-likelihood estimates of the amplitude variability and signal waveform via a generalized Expectation-Maximization algorithm. The expectation step involves a Kalman fixed-interval smoother which tracks the trial-to-trial amplitude variability while the maximization step estimates the signal waveform, spatial noise covariance, and LDSR model parameters. We demonstrate the effectiveness of the proposed model using both real and simulated evoked response data. The performance of the algorithm is analyzed in terms of the mean squared error of the amplitude estimates.
Starting Page	151
Ending Page	155
File Size	1303157
Page Count	5
File Format	PDF
ISBN	9781424411979
DOI	10.1109/SSP.2007.4301237
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2007-08-26
Publisher Place	USA
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Brain modeling Electroencephalography Amplitude estimation Fatigue Chromium Spatiotemporal phenomena Background noise Covariance matrix Maximum likelihood estimation Expectation-maximization algorithms Trial variability Event-related MEG Expectation-maximization Linear dynamic model Maximum likelihood
Content Type	Text
Resource Type	Article

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