A two-way regularization method for MEG source reconstruction
نویسندگان
چکیده
منابع مشابه
A Two-way Regularization Method for Meg Source Reconstruction
The MEG inverse problem refers to the reconstruction of the neural activity of the brain from magnetoencephalography (MEG) measurements. We propose a two-way regularization (TWR) method to solve the MEG inverse problem under the assumptions that only a small number of locations in space are responsible for the measured signals (focality), and each source time course is smooth in time (smoothnes...
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Magnetoencephalography (MEG) is a common noninvasive imaging modality for instantaneously measuring whole brain activities. One challenge in MEG data analysis is how to minimize the impact of the outliers that commonly exist in the images. In this paper, we propose a robust two-way regularization approach to solve the important MEG inverse problem, i.e., reconstructing neuronal activities using...
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We describe a simple and efficient solution to the problem of reconstructing electromagnetic sources into a canonical or standard anatomical space. Its simplicity rests upon incorporating subject-specific anatomy into the forward model in a way that eschews the need for cortical surface extraction. The forward model starts with a canonical cortical mesh, defined in a standard stereotactic space...
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Our understanding of the dynamics of neuronal activity in the human brain remains limited, due in part to a lack of adequate methods for reconstructing neuronal activity from noninvasive electrophysiological data. Here, we present a novel adaptive time-varying approach to source reconstruction that can be applied to magnetoencephalography (MEG) and electroencephalography (EEG) data. The method ...
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NUTMEG is an open-source MATLAB-based toolbox for MEG/EEG data. NUTMEG includes many options for source reconstruction, an easily navigable window for exploring source results, several options for source level connectivity computation, statistical evaluation of these source results, and conversion to and from formats of other toolboxes.
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ژورنال
عنوان ژورنال: The Annals of Applied Statistics
سال: 2012
ISSN: 1932-6157
DOI: 10.1214/11-aoas531