Bayesian blind source separation for brain imaging

This paper deals with the problem of blind source separation in fMRI data analysis. Our main contribution is to present a maximum likelihood based method to blindly separate the brain activations in an fMRI experiment. Choosing the time frequency domain as the signal representation space, our method relies on the second order statistics and exploits the intersource diversity. It is efficiently implemented by the EM (Expectation-Maximization) algorithm where the time courses of the brain activations are considered as the hidden variables. The estimation variance of the STFT (Short Time Fourier Transform) is reduced by averaging across time frequency sub-domains. The successful separation of the right and left visual cortex activations during a visual fMRI experiment, in a block design, and the extraction of only the relevant tasks corroborate the effectiveness of our proposed separating algorithm.