## The 15th Statistical Machine Learning Seminar (2014.1.23)

第15回統計的機械学習セミナー

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)

Access: http://www.ism.ac.jp/access/index_e.html

Title: Testing independent components, with applications to brain imaging

Speaker: Aapo Hyvarinen (University of Helsinki)

Abstract:

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

EEG/MEG and fMRI.