The 15th Statistical Machine Learning Seminar (2014.1.23)

The 15th Statistical Machine Learning Seminar

Date/time: Janu. 23 (Thurs) 15:00-16:30
Place: Seminar Room 5 (3F, D313 & D314),
Institute of Statistical Mathematics (Tachikawa, Tokyo)

Title: Testing independent components, with applications to brain imaging
Speaker: Aapo Hyvarinen (University of Helsinki)

Independent component analysis (ICA) is increasingly used for analyzing
brain imaging data. ICA typically gives a large number of components
many of which may be just random, due to insufficient sample size,
violations of the model, or algorithmic problems. Few methods are
available for computing the statistical significance (reliability) of
the components. We propose to approach this problem by performing ICA
separately on a number of subjects, and finding components which are
sufficiently consistent (similar) over subjects. Similarity can be
defined in two different ways: 1) the similarity of the mixing
coefficients, which usually correspond to spatial patterns in EEG and
MEG, or 2) the similarity of the independent components themselves,
which usually correspond to spatial patterns in fMRI. The threshold of
what is “sufficient” is rigorously defined by a null hypothesis under
which the independent components are random orthogonal components in the
whitened space. Components which are consistent in different subjects
are found by clustering under the constraint that a cluster can only
contain one source from each subject, and by constraining the number the
false positives based on the null hypothesis. Instead of different
subjects, the method can also be applied on different sessions of
recordings from a single subject. The methods are applicable to both