We analyze the complex networks associated with brain electrical activity.
Multichannel EEG measurements are first processed to obtain 3D voxel
activations using the tomographic algorithm LORETA. Then, the correlation of
the current intensity activation between voxel pairs is computed to produce a
voxel cross-correlation coefficient matrix. Using several correlation
thresholds, the cross-correlation matrix is then transformed into a network
connectivity matrix and analyzed. To study a specific example, we selected data
from an earlier experiment focusing on the MMN brain wave. The resulting
analysis highlights significant differences between the spatial activations
associated with Standard and Deviant tones, with interesting physiological
implications. When compared to random data networks, physiological networks are
more connected, with longer links and shorter path lengths. Furthermore, as
compared to the Deviant case, Standard data networks are more connected, with
longer links and shorter path lengths--i.e., with a stronger ``small worlds''
character. The comparison between both networks shows that areas known to be
activated in the MMN wave are connected. In particular, the analysis supports
the idea that supra-temporal and inferior frontal data work together in the
processing of the differences between sounds by highlighting an increased
connectivity in the response to a novel sound.Comment: 22 pages, 5 figures. Starlab preprint. This version is an attempt to
include better figures (no content change
