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X-WR-CALNAME;VALUE=TEXT:Eventi DIAG
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TZID:Europe/Paris
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DTSTART:20121028T030000
TZOFFSETFROM:+0200
TZOFFSETTO:+0100
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DTSTART:20130331T020000
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UID:calendar.6448.field_data.0@diag.uniroma1.it
DTSTAMP:20260403T174031Z
CREATED:20121213T112115Z
DESCRIPTION:Electroneurography (ENG) is a minimally invasive method of iden
 tifying activation of separate neuron fascicles within a peripheral nerve 
 from the measurements voltage distributions on the surface of the nerve. E
 NG can be applied to control a robotic prosthetic arm by the intact periph
 eral nerves of an amputation patient\, or to gain muscle control of a spin
 al chord injury patient. Mathematically\, the ENG problem is an ill-posed 
 inverse problem with high degree of non-uniqueness and instability\, and t
 he signal suffers from a low signal-to-noise ratio. In this talk\, we disc
 uss the computational model\, the beamformer algorithm that has been sugge
 sted for the ENG problem\, and a Bayesian hierarchical version of it\, all
 owing a noise reduction method to improve the identification of the active
  fascicles.D. CALVETTIBayesian source separation in MEGAbstract:Magnetoenc
 ephalography (MEG) is a completely non-invasive brain-mapping modality whi
 ch uses measurements of the magnetic field outside the head induced by ele
 ctrical brain activity to localize and characterize the activity inside th
 e brain. Potentially\, it is particularly useful in the study of epilepsy 
 as a tool for localizing the foci of the onset of seizures. A key issue in
  MEG is the separation of sources of a different nature. Non-focal sources
  from both inside and outside of the brain produce interference\, making t
 he inverse problem of identifying the focal source signal extremely diffic
 ult. In this talk we show how Bayesian methods can be used to address this
  issue. In particular\, we illustrate how a mixed prior distribution is ab
 le to separate sources which are statistically different from each other. 
 Furthermore\, we propose using a depth scan to identify activity from deep
  focal sources. Numerical simulations are used to generate controlled data
  in order to validate the model.
DTSTART;TZID=Europe/Paris:20121213T120000
DTEND;TZID=Europe/Paris:20121213T120000
LAST-MODIFIED:20191008T084958Z
LOCATION:Aula Magna DIAG\, via Ariosto 25\, I floor
SUMMARY:Seminari MORE@DIAG - Erkki SOMERSALO\, Case Western Reserve\, Depar
 tment of Mathematics\, Cleveland OH
URL;TYPE=URI:http://diag.uniroma1.it/node/6448
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