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Functional connectivity of cognitive tasks allows researchers to analyse theinteraction mapping occurring between different regions of the brain usingelectroencephalography (EEG) signals. Standard practice in functionalconnectivity involve studying the electrode pair interactions across severaltrials. As the cognitive task always involves the human factor, it isinevitable to have lower quality data from the brain signals influenced by thesubject concentration or other mental states which can occur anytime over thewhole experimental trials. The connectivity among electrodes are heavilyinfluenced by these low quality EEG. In this paper, we aim at enhancing thefunctional connectivity of mental tasks by implementing a classification stepin the process to remove those incorrect EEG trials from the available set. Theclassification step removes the trials which were mis-classified or had a lowprobability of occurrence to extract only reliable EEG trials. Through ourapproach, we have successfully improved the separability among graph parametersfor different mental tasks. We also observe an improvement in the readabilityof the connectivity by focusing only on a group of selected channels ratherthan employing all the channels.

Systematic Enhancement of Functional Connectivity in Brain-Computer Interfacing using Common Spatial Patterns and Tangent Space Mapping