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| We are going to use the protocol '''TutorialRaw''' created in the previous tutorial [[C3. Epoching and averaging]]. If you have not followed this tutorial yet, please do it now. | We are going to use the protocol '''!TutorialRaw''' created in the previous tutorial [[Tutorials/TutRawAvg|Epoching and Averaging]]. If you have not followed this tutorial yet, please do it now. |
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| Beamfoming methods are | Beamfoming methods scan each target voxel/vertex position <<latex($\bf r$)>> to estimate a spatial filter <<latex(${\bf W}_{\bf r}$)>> which outputs the source activity <<latex($y(t)$)>>: <<latex(\equation{begin} y(t) = {\bf W}_{\bf r}^{\rm T}\bf{m}(t) \equation{end})>> |
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 will show how to apply beamforming methods to MEG data and obtain the statistic map of source activation.
We are going to use the protocol TutorialRaw created in the previous tutorial ?Epoching and Averaging. If you have not followed this tutorial yet, please do it now.
Contents
Introduction
Beamfoming methods scan each target voxel/vertex position 
to estimate a spatial filter 
which outputs the source activity 
:
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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