Regularized spectral matching for blind source separation

Application to fMRI imaging

Hichem Snoussi (1) , Vince D. Calhoun (2)

1 M2S - Modélisation et Sûreté des Systèmes
2 Yale University [New Haven]

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.

Domains: Signal and Image processing

Hichem Snoussi, Vince D. Calhoun. Regularized spectral matching for blind source separation. IEEE Transactions on Signal Processing, 2005, 53 (9), pp.3373-3383. ⟨10.1109/TSP.2005.853209⟩. ⟨hal-02359105⟩