期刊
NEUROIMAGE
卷 56, 期 1, 页码 78-92出版社
ACADEMIC PRESS INC ELSEVIER SCIENCE
DOI: 10.1016/j.neuroimage.2011.02.002
关键词
MEG; EEG; Denoising; Whitening; Spectral and time domain analysis; Source imaging; Signal space separation; Subspace projection
资金
- French National Research Agency (Agence Nationale pour la Recherche) [ANR-08-BLAN-0250]
- Agence Nationale de la Recherche (ANR) [ANR-08-BLAN-0250] Funding Source: Agence Nationale de la Recherche (ANR)
MEG and EEG data contain additive correlated noise generated by environmental and physiological sources. To suppress this type of spatially coloured noise, source estimation is often performed with spatial whitening based on a measured or estimated noise covariance matrix. However, artifacts that span relatively small noise subspaces, such as cardiac, ocular, and muscle artifacts, are often explicitly removed by a variety of denoising methods (e.g., signal space projection) before source imaging. Here, we introduce a new approach, the spectral signal space projection ((SP)-P-3) algorithm, in which time-frequency (TF)-specific spatial projectors are designed and applied to the noisy TF-transformed data, and whitened source estimation is performed in the TF domain. The approach can be used to derive spectral variants of all linear time domain whitened source estimation algorithms. The denoised sensor and source time series are obtained by the corresponding inverse IF-transform. The method is evaluated and compared with existing subspace projection and signal separation techniques using experimental data. Altogether, S3P provides an expanded framework for MEG/EEG data denoising and whitened source imaging in both the time and frequency/scale domains. (C) 2011 Elsevier Inc. All rights reserved.
作者
我是这篇论文的作者
点击您的名字以认领此论文并将其添加到您的个人资料中。
推荐
暂无数据