  Functional connectivity of cognitive tasks allows researchers to analyse the
interaction mapping occurring between different regions of the brain using
electroencephalography (EEG) signals. Standard practice in functional
connectivity involve studying the electrode pair interactions across several
trials. As the cognitive task always involves the human factor, it is
inevitable to have lower quality data from the brain signals influenced by the
subject concentration or other mental states which can occur anytime over the
whole experimental trials. The connectivity among electrodes are heavily
influenced by these low quality EEG. In this paper, we aim at enhancing the
functional connectivity of mental tasks by implementing a classification step
in the process to remove those incorrect EEG trials from the available set. The
classification step removes the trials which were mis-classified or had a low
probability of occurrence to extract only reliable EEG trials. Through our
approach, we have successfully improved the separability among graph parameters
for different mental tasks. We also observe an improvement in the readability
of the connectivity by focusing only on a group of selected channels rather
than employing all the channels.
