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Separating Sources and Analysing Connectivity in EEG/MEG Using Probabilistic Models

Published on Dec 03, 20123832 Views

Aapo Hyvärinen

Currently, there is increasing interest in analysing brain activity in resting state, or under relatively natural conditions such as while watching a movie. When using functional magnetic resonance im

BBCI Summer School

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Machine LearningNeuroscienceNeurotechnology

Chapter list

Separating sources and analysing connectivity in EEG/MEG using probabilistic models00:00
Abstract00:13
Problem of blind source separation01:57
A solution is possible (1)02:56
A solution is possible (2)03:14
Independent Component Analysis04:27
When can the ICA model be estimated?05:40
Reminder: Principal component analysis07:24
Comparison of ICA, factor analysis and principal component analysis09:05
Some examples of nongaussianity 12:00
Why classic methods cannot find original components or sources13:54
Nongaussianity, with independence, gives more information15:44
Illustration18:32
Illustration of problem with gaussian distributions20:03
Basic intuitive principle of ICA estimation22:24
Illustration of changes in nongaussianity27:11
Development of ICA algorithms28:04
Sparsity is the dominant form of non-Gaussianity 31:34
Combining ICA with factor analysis or PCA33:57
The brain at rest37:19
Is anything happening in the brain at rest?39:53
ICA finds resting-state networks in fMRI (1)41:28
ICA finds resting-state networks in fMRI (2)43:02
How about EEG and MEG?43:50
Different sparsities of EEG/MEG data (1)44:28
Different sparsities of EEG/MEG data (2)45:34
Different sparsities of EEG/MEG data (3)46:16
Different sparsities of EEG/MEG data (4)47:04
Spectral sparsity: Fourier-ICA (1)47:36
Spectral sparsity: Fourier-ICA (2)49:33
Spatial sparsity (spatial ICA)50:54
Spatial ICA in MEG52:27
Testing ICs: motivation (1)54:54
Testing ICs: motivation (2)57:54
Testing ICs: results01:00:49
Causal analysis: Introduction01:02:52
Structural equation models (1)01:08:26
Structural equation models (2)01:09:47
Simple measures of causal direction01:11:05
Sample of results on MEG01:13:46
Discussion (1)01:15:05
Discussion (2)01:16:22
Discussion (3)01:17:03