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Inverse Methods for EEG and MEG Source Reconstruction
Published on Dec 03, 20129232 Views
In this lecture we review the most popular inverse methods for EEG and MEG source reconstruction. Inverse methods can be divided into three different catagories: a) overdetermined models, b) underdete
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Chapter list
Tutorial on EEG/MEG inverse source reconstruction00:00
EEG and MEG (1)00:37
EEG and MEG (2)01:41
Volume conduction02:29
Interpretability03:35
Characteristics of the EEG and MEG generation (1)04:59
Characteristics of the EEG and MEG generation (2)05:51
Characteristics of the EEG and MEG generation (3)06:28
Generative model of the EEG06:54
Forward modeling08:24
The Inverse Problem (1)09:13
The Inverse Problem (2)09:22
Source Reconstruction Paradigms09:48
Inverse methods10:40
Dipole modeling (1)10:58
Dipole modeling (2)11:00
Dipole Modeling, high noise11:53
Dipole Modeling, brain noise12:17
What is minimized? (1)12:59
What is minimized? (2)13:52
The Problem of Local Minima (1)14:37
The Problem of Local Minima (2)15:11
Including time16:23
Example: Event-related Potentials (ERP)17:37
Distributed Inverse Imaging (1)18:30
Distributed Inverse Imaging (2)18:33
Cost Function19:40
Constraints21:16
Spatial smoothness23:07
Origin of blurring24:46
Spatial sparsity25:29
Origin of sparsity26:48
No sparsity using L2-norm28:20
Limitations of smooth (linear) and sparse inverses28:43
Alternative constraints (1)30:29
Alternative constraints (2)31:34
Real-world Example32:00
Depth compensation (1)33:14
More „physiological“ constraints33:27
Summary (1. part)34:57
Part II35:48
Beamformers36:23
Dipole amplitude? (1)36:25
Dipole amplitude? (2)41:43
We measure source + noise How do we know the noise level?43:30
Nulling beamformer47:55
SAM and LCMV beamformer51:32
DICS=LCMV beamformer in frequency domain54:10
EEG-simulation of ERD (1 source) -154:54
EEG-simulation of ERD (1 source) -256:00
EEG-simulation of ERD (1 source) -358:03
MUSIC (Multiple Signal Classification)58:45
1. Find important patterns in data: PCA of covariance matrix (1)01:00:45
1. Find important patterns in data: PCA of covariance matrix (2)01:02:04
1. Find important patterns in data: PCA of covariance matrix (3)01:02:19
2. Does a combination of eigenvectors look like a dipole at a some location? (1)01:02:49
2. Does a combination of eigenvectors look like a dipole at a some location? (2)01:04:16
Scan: one slice01:04:59
Scan: whole brain01:05:27
Illustration for P=201:05:55
Truth - Music01:07:15
RAP-MUSIC01:08:20
Source explains data01:10:22
The End01:12:21