[BIC-announce] MCIN lecture (open to all) - "EEG based brain connectivity"

[Patrick Bermudez]

We are very pleased to welcome senior scientist Dr. Bosch-Bayard to
the MCIN, BIC, and MNI community and invite you to join us this
Friday, the 25th of January, at 13:00 in the de Grandpré auditorium
for a lecture he will give entitled "EEG based brain connectivity".
Here is an abstract of this talk:

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Brain connectivity based in EEG data recorded at the scalp is affected
by various negative factors, being the volume conduction effect one of
the most important. The EEG does not measure the activity of the
neurons directly located under the electrode in the gray matter, but
of wide areas of the brain. The signal gathered at the scalp is an
attenuated mixture of the true neuronal activity. At the sources the
connectivity is also problematic due to the low resolution of the
inverse methods. Any EEG inverse solution provides a source estimate
at each voxel that is a mixture of the true source values over all
voxels of the brain. This mixing effect usually causes notable
distortion in estimates of source connectivity based on inverse
solutions. To lessen this shortcoming, an unmixing approach for EEG
linear inverse methods is presented here. It is based on a piecewise
approximation of the unknown sources by means of a brain segmentation
formed by specified regions (ROIs). The approach is general and
flexible enough to be applied to any linear inverse method, with any
specified family of ROIs, including point, surface and 3D brain
regions. Two of its variants are elaborated: arbitrary piecewise
constant sources over arbitrary regions and sources with piecewise
constant intensity of known direction over cortex surface regions.
Numerically, the approach requires just solving a system of linear
equations. Advantages and variants of this approach for connectivity
analysis are discussed through a variety of simulated examples.
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