Abstract
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 compone ts and considers the remaining components (artifacts) to be noise.
Original language | English (US) |
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Pages (from-to) | 3373-3383 |
Number of pages | 11 |
Journal | IEEE Transactions on Signal Processing |
Volume | 53 |
Issue number | 9 |
DOIs | |
State | Published - Sep 2005 |
Externally published | Yes |
Keywords
- Blind source separation
- EM algorithm
- Maximum likelihood
- Short-time Fourier transform
- fMRI imaging
ASJC Scopus subject areas
- Signal Processing
- Electrical and Electronic Engineering