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...e estimated correctly. They need not be estimated with any great precision: in fact it is enough to estimate whether they are sub- or supergaussian (Cardoso and Laheld, 1996; Hyvärinen and Oja, 1998; =-=Lee et al., 1999-=-). In many cases, in fact, we have enough prior knowledge on the independent components, and we don’t need to estimate their nature from the data. In any case, if the information on the nature of the ...

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...d, ICA can be seen as doing something akin to nonorthogonal PCA and to cluster analysis, however, when the source models are sub-Gaussian, the relationship between these techniques is less clear. See =-=[30]-=- for a discussion of ICA in the context of sub-Gaussian sources. B. Two Architectures for Performing ICA on Images Let be a data matrix with rows and columns. We can think of each column of as outcome...

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...use line noise is subGaussian, the original ICA algorithm ~Bell & Sejnowski, 1995!, without the extension to sub-Gaussian sources, did not coalesce the line noise in the data into a single component ~=-=Lee et al., 1999-=-!. ICA decomposition may be useful as well for observing fine details of the spatial structure of ongoing EEG activity in multiple brain areas or neural populations ~Jung et al., 1997; Makeig, Jung, B...

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...gs from the human brain. Keywords—Blind source separation, EEG, fMRI, independent component analysis. I. INTRODUCTION Independent component analysis (ICA) refers to a family of related algorithms [1=-=]–[10]-=- that exploit independence to perform blind source separation. In Section II, an ICA algorithm based on the Infomax principle [6] is briefly introduced. In Section III, ICA is applied to electroenceph...

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...fact components that could not be localized (12%). presence of eye-blink or eye-movement artifacts was not a criterion for rejection. All remaining data epochs were submitted to extended infomax ICA (=-=Lee et al., 1999-=-) using runica (Makeig et al., 1997) from the EEGLAB toolbox. ICA (Bell and Sejnowski, 1995; Makeig et al., 1996) finds an Funmixing_ matrix (W) that linearly unmixes the original EEG channel data (x)...

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...py, between the joint density of {Y 1 , . . . ,Y D } and the product of its marginals. The utility of (1) for purposes of the ICA problem has been well documented in the literature (c.f. Comon, 1994, =-=Lee et al., 1999-=-a). Briefly, we note that for mutually independent random variablessY 1 ,Y 2 , ...,Y D we have J(Y ) = # p(y 1 , y 2 , ..., y D )log p(y 1 , y 2 , ..., y D ) p(y 1 )p(y 2 )...p(y D ) d = # p(y 1 , y 2...

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...enation across subjects within groups, the dimensions were again reduced to the optimal numbers via the second round of PCA. Then, the data were separated by ICA using the Extended Infomax algorithm (=-=Lee et al., 1999-=-). After ICA separation, the mean ICs and the corresponding mean time courses over all of the subjects were used for the back-reconstruction of the ICs and time courses for each individual subject (Ca...