|
Size: 1192
Comment:
|
Size: 1215
Comment:
|
| Deletions are marked like this. | Additions are marked like this. |
| Line 6: | Line 6: |
| We are going to use the protocol '''TutorialRaw''' created in the previous tutorial [[Tutorials/TutRawAvg]]. 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. |
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 are
Spatial filter
Text
Section 2
Text
Linearly-constrained minimum variance beamformer (LCMV)
Section 1
Text
Section 2
Text
Maximum constrast beamformer (MCB)
Section 1
Text
Section 2
Text
Beamformer-based correlation/coherence imaging
Dynamic imaging of coherent sources (DICS)
Text
Spatiotemporal imaging of linearly-related source components (SILSC)
Text