  Magnetoencephalographic (MEG) measurements record magnetic fields generated
from neurons while information is being processed in the brain. The inverse
problem of identifying sources of biomagnetic fields and deducing their
intensities from MEG measurements is ill-posed when the number of field
detectors is far less than the number of sources. This problem is less severe
if there is already a reasonable prior knowledge in the form of a distribution
in the intensity of source activation. In this case the problem of identifying
and deducing source intensities may be transformed to one of using the MEG data
to update a prior distribution to a posterior distribution. Here we report on
some work done using the maximum entropy method (ME) as an updating tool.
Specifically, we propose an implementation of the ME method in cases when the
prior contain almost no knowledge of source activation. Two examples are
studied, in which part of motor cortex is activated with uniform and varying
intensities, respectively.
