Blind Source Separation with Sparsity Constraints for Magnetoencephalography
Abstract
Several phenomena are a superposition of basic events, in that it is important to identify the sources of these events and their contribution to the overall phenomena. Blind Source Separation (BSS) aim to reveal unknown sources assuming: a) the observed signals and the source activity are linearly dependent; b) the sources are a stochastic process.
We present a new mathematical model based on BSS with sparsity for Magnetoencephalography, the scope of which is the identification of neural currents taking place during brain activity. Since only few areas of the brain are active at the same time, sparsity characterizes the problem better than the sole BSS assumption.
[DOI: 10.1685/CSC06082] About DOI
We present a new mathematical model based on BSS with sparsity for Magnetoencephalography, the scope of which is the identification of neural currents taking place during brain activity. Since only few areas of the brain are active at the same time, sparsity characterizes the problem better than the sole BSS assumption.
[DOI: 10.1685/CSC06082] About DOI
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[DOI: 10.1685/] About DOI
Url Resolver: : http://dx.doi.org/10.1685/
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