Aapo Hyvärinen: Publications

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Brain imaging data analysis Bayesian networks & SEM Estimation theory Natural image statistics The FastICA algorithm Independent component analysis: misc. Unsupervised learning: misc. Experimental vision research   Publications front page Back to home page

Blind separation of sources with temporal structure

[As an alternative to independent component analysis, these methods can be used to blindly separate sources, assuming that they have temporal correlations]

A. Hyvärinen, P. Ramkumar, L. Parkkonen, and R. Hari. Independent component analysis of short-time Fourier transforms for spontaneous EEG/MEG analysis. NeuroImage, 49(1):257-271, 2010.
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[Proposes a source separation method tailor-made to EEG and MEG signals. Basically, preprocess the data by short-time Fourier transforms and then do ICA. Shows that this takes temporal correlations into account, and combines them with non-Gaussianity.]

A. Hyvärinen. A unifying model for blind separation of independent sources. Signal Processing, 85(7):1419-1427, 2005.
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[A framework and algorithm that unifies the three commonly used separation principles: nongaussianity, nonstationary variances, and second-order autocorrelations.]

A. Hyvärinen and J. Hurri. Blind separation of sources that have spatiotemporal dependencies. Signal Processing, 84(2):247-254, 2004 (special issue on nonlinear and non-independent source separation).
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[Introduces double-blind source separation, which means separation of sources that are not independent, without a parametric model of the dependencies. Uses a cumulant-based criterion similar to our temporal coherence models of natural image sequences.]

A. Hyvärinen. Complexity Pursuit: Separating interesting components from time-series. Neural Computation, 13(4):883--898, 2001.
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[Introduces the concept of complexity pursuit, which means finding projections of time series (signals) that have minimum complexity. Also introduces simple approximations of complexity that take into account both nongaussianity and autocorrelations.]

A. Hyvärinen. Blind source separation by nonstationarity of variance: A cumulant-based approach. IEEE Trans. on Neural Networks, 12(6):1471-1474, 2001.
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[Formulates the less-known separation criterion on variance nonstationarity using cumulants, and proposes a fast fixed-point algorithm.]

A. Hyvärinen. Independent Component Analysis for Time-dependent Stochastic Processes.  In Proc. Int. Conf. on Artificial Neural Networks (ICANN'98), Skövde, Sweden, pp. 541-546, 1998.
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[This paper shows that in ICA, it is often useful to preprocess the data by computing the innovation processes.]