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The Bayesian ICA algorithm was tested on MEG data, which is identical
to the data used in [10]. The data has 122 channels of
measurements over two minutes digitized at 148 Hz. The measurements
contain signals resulting in the electrical activity of the brain but
also signals which can be considered artifacts. These include signals
caused by muscular activity, eye movements, cardiac rhythm and even
a signal caused by a digital watch that the test subject was wearing.
Since it can be assumed that most of the artifacts are independent of
the brain activity, it is hoped that ICA can find the artifacts.
The Bayesian ICA algorithm was used to separate 30 sources from the
122 measures channels. The results obtained were comparable to those
reported in [10]. In figure 1, five measurements and
five nonGaussian sources found by the algorithm are illustrated.
Figure 1:
Above: five MEG measurements. Below: five separated sources found by
the Bayesian ICA algorithm.

The modification of the estimate of a by the estimate
a_{G}typically reduces the convergence time by a factor of ten; the iteration
typically converged in 30 iterations.
Next: Discussion
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Previous: Overview of the Bayesian
Harri Lappalainen
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