第15回統計的機械学習セミナー / The 15th Statistical Machine Learning Seminar

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.