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Regularized Spectral Matching for Blind Source Separation. Application to fMRI Imaging

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2 Author(s)
Snoussi, H. ; M2S/ISTIT Lab., Univ. of Technol. of Troyes, France ; Calhoun, V.D.

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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Signal Processing, IEEE Transactions on  (Volume:53 ,  Issue: 9 )