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A graphical model for estimating stimulus-evoked brain responses from magnetoencephalography data with large background brain activity.

Abstract：

:This paper formulates a novel probabilistic graphical model for noisy stimulus-evoked MEG and EEG sensor data obtained in the presence of large background brain activity. The model describes the observed data in terms of unobserved evoked and background factors with additive sensor noise. We present an expectation maximization (EM) algorithm that estimates the model parameters from data. Using the model, the algorithm cleans the stimulus-evoked data by removing interference from background factors and noise artifacts and separates those data into contributions from independent factors. We demonstrate on real and simulated data that the algorithm outperforms benchmark methods for denoising and separation. We also show that the algorithm improves the performance of localization with beamforming algorithms.

journal_name

Neuroimage

journal_title

NeuroImage

authors

Nagarajan SS,Attias HT,Hild KE 2nd,Sekihara K

doi

10.1016/j.neuroimage.2005.09.055

keywords:

subject

Has Abstract

pub_date

2006-04-01 00:00:00

pages

400-16

issue

2

eissn

1053-8119

issn

1095-9572

pii

S1053-8119(05)00762-7

journal_volume

30

pub_type

杂志文章

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