Adaptive Sparse Representation for Source Localization with Gain/Phase Errors

Adaptive Sparse Representation for Source Localization with Gain/Phase Errors

Sparse representation (SR) algorithms can be implemented for high-resolution direction of arrival (DOA) estimation. Additionally, SR can effectively separate the coherent signal sources because the spectrum estimation is based on the optimization technique, such as the L(1) norm minimization, but not on subspace orthogonality. However, in the actual source localization scenario, an unknown gain...

Source Extraction Using Sparse Representation

Sparse Bayesian Learning for EEG Source Localization

Purpose: Localizing the sources of electrical activity from electroencephalographic (EEG) data has gained considerable attention over the last few years. In this paper, we propose an innovative source localization method for EEG, based on Sparse Bayesian Learning (SBL). Methods: To better specify the sparsity profile and to ensure efficient source localization, the proposed approach considers g...

Source localization of error negativity: additional source for corrected errors.

Error processing in corrected and uncorrected errors was studied while participants responded to a target surrounded by flankers. Error-related negativity (ERN/NE) was stronger and appeared earlier in corrected errors than in uncorrected errors. ERN neural sources for each error type were analyzed using low-resolution electromagnetic tomography method of source localization. For corrected error...

Blind Source Separation via Multinode Sparse Representation

We consider a problem of blind source separation from a set of instantaneous linear mixtures, where the mixing matrix is unknown. It was discovered recently, that exploiting the sparsity of sources in an appropriate representation according to some signal dictionary, dramatically improves the quality of separation. In this work we use the property of multi scale transforms, such as wavelet or w...