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One of the biggest challenges in the field of computational neuroscience from the perspective of complex network analysis is the measurement of dynamic local and global interactions of the brain regions during cognitive function. Graph theoretic analysis has been extensively applied to study the dynamics of functional brain networks in the recent years. The selection of appropriate thresholding methods to construct weighted/unweighted subnetworks to detect cognitive load induced changes in brain׳s electrical activity remains an open challenge in the functional brain network research. This paper reviews the application of statistical and information theoretic metrics to construct the functional brain networks, proposes a novel Branch-and-Bound based thresholding algorithm that extracts the influential subnetwork, and applies efficient computational techniques and complex network metrics to detect and quantify the cognitive activities. The empirical analyses showcase the efficiency of the proposed thresholding algorithm by highlighting the changing neuronal patterns during cognitive activity when compared to that of baseline activity. Statistical evaluation of the results further validates the efficiency of the proposed method as well. The results demonstrate the ability of the proposed algorithm in detecting subtle cognitive load induced changes in functional brain networks Refereed/Peer-reviewed

Reducing a process to its component units underscores scientific research, yet such a pre-occupation with the details of human cognition may mean that the bigger picture is missed. By adopting a global approach to cognitive processing, the current study aimed to identify cortical locations capable of accurately reflecting global cognitive load; that is, overall cognitive processing. Working on the assumption that higher and lower order cognitive processes have limited processing capacity, and that interactions between these processes will have at least an additive effect on performance degradation,results suggested that global cognitive load is measureable in real time using

Mental health promotion is one of the major research challenges in Healthcare industries.Systematic investigation of measures and metrics to make reliable inferences becomes essential in the neurological analysis of mental disorders. This research work focuses on gaining deeper insights into the complex neurological interactions and changes that occur in many mental conditions and disorders by proficient analysis of the functional brain network properties. It provides an overview of applying graph theoretic and statistical metrics to represent Electroencephalograph (EEG) data and further identifying changes in cognitive activities during normal/idle and cognitive load conditions using graph pattern mining techniques. The empirical results of graph analysis of the EEG data in time and frequency domains reveal hidden, non-trivial, time-varying neuronal network patterns demonstrating interesting changes like strengthening of correlations between many of the brain regions and development of new neural connections, development of highly cohesive clusters (cliques) in certain regions during cognition