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| What is the purpose of this tutorial? What data is it based on? | The estimation of source distribtion is an important step to understand the brain activity from EEG and MEG data. Dipole fitting, minimum norm estimation, and beamformer are three commonly used methods. It has been proved that beamforming methods provide good spatial resolution. This tutorial introduces beamforming methods, which are commonly used to estimate source distribution from EEG and MEG data. This We are going to use the protocol '''TutorialRaw''' created in the previous tutorial [[http://neuroimage.usc.edu/brainstorm/Tutorials/TutRawViewer|Review continuous recordings and edit markers]]. If you have not followed this tutorial yet, please do it now. |
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| Introduction to beamforming methods (a little background) | Beamfoming methods are |
Beamforming methods
Authors: Hui-Ling Chan
The estimation of source distribtion is an important step to understand the brain activity from EEG and MEG data. Dipole fitting, minimum norm estimation, and beamformer are three commonly used methods. It has been proved that beamforming methods provide good spatial resolution. This tutorial introduces beamforming methods, which are commonly used to estimate source distribution from EEG and MEG data.
This
We are going to use the protocol TutorialRaw created in the previous tutorial Review continuous recordings and edit markers. If you have not followed this tutorial yet, please do it now.
Contents
Introduction
Beamfoming methods are
Spatial filter
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Section 2
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Linearly-constrained minimum variance beamformer (LCMV)
Section 1
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Section 2
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Maximum constrast beamformer (MCB)
Section 1
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Section 2
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Beamformer-based correlation/coherence imaging
Dynamic imaging of coherent sources (DICS)
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Spatiotemporal imaging of linearly-related source components (SILSC)
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