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SUMMARY:Bayesian preconditioning for truncated Krylov subspace regularizat
 ion with an application to Magnetoencephalography (MEG) - Somersalo\, E (C
 ase Western Reserve University)
DTSTART:20140211T110000Z
DTEND:20140211T114500Z
UID:TALK50803@talks.cam.ac.uk
CONTACT:Mustapha Amrani
DESCRIPTION:Co-authors: Daniela Calvetti (Case Western Reserve University)
 \, Laura Homa (Case Western Reserve University) \n\nWe consider the comput
 ational problem arising in magnetoencephalography (MEG)\, where the goal i
 s to estimate the electric activity within the brain non-invasively from e
 xtra-cranial measurements of the magnetic field components. The problem is
  severely ill-posed due to the intrinsic non-uniqueness of the solution\, 
 and suffer further from the challenges of starting from a weak data signal
 \, its high dimensionality and complexity of the noise\, part of which is 
 due to the brain itself. We propose a new algorithm that is based on trunc
 ated conjugate gradient algorithm for least squares (CGLS) with statistica
 lly inspired left and right preconditioners. We demonstrate that by carefu
 lly accounting for the spatiotemporal statistical structure of the brain n
 oise\, and by adopting a suitable prior within the Bayesian framework\, we
  can design a robust and efficient method for the numerical solution of th
 e MEG inverse problem which can improve the spatial and temporal resolutio
 n of events of short duration.\n
LOCATION:Seminar Room 1\, Newton Institute
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