Regularized Spectral Matching for Blind Source Separation. Application to fMRI Imaging
Snoussi, H.; Calhoun, V.D.
Signal Processing, IEEE Transactions on
Volume 53, Issue 9, Sept. 2005 Page(s): 3373 - 3383
Digital Object Identifier 10.1109/TSP.2005.853209
Summary: The main contribution of this paper is to present a Bayesian approach for solving the noisy instantaneous blind source separation problem based on second-order statistics of the time-varying spectrum. The success of the blind estimation relies on the nonstationarity of the second-order statistics and their intersource diversity. Choosing the time-frequency domain as the signal representation space and transforming the data by a short-time Fourier transform (STFT), our method presents a simple EM algorithm that can efficiently deal with the time-varying spectrum diversity of the sources. The estimation variance of the STFT is reduced by averaging across time-frequency subdomains. The algorithm is demonstrated on a standard functional resonance imaging (fMRI) experiment involving visual stimuli in a block design. Explicitly taking into account the noise in the model, the proposed algorithm has the advantage of extracting only relevant task-related components and considers the remaining components (artifacts) to be noise.
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