Recent advances in computational neuroscience have enabled trans-disciplinary researchers to address challenging tasks such as the identification and characterization of cognitive function in the brain. The application of graph theory has contributed to the modelling and understanding the brain dynamics. This paper presents a new approach based on a special graph theoretic concept called Minimum Connected Component (MCC) to detect cognitive load induced changes in functional brain networks using EEG data. The results presented in this paper clearly demonstrate that the MCC based analysis of the functional brain networks derived from multi-channel EEG data is able to detect and quantify changes across the scalp in response to specific cognitive tasks. The MCC, due to its sensitivity to cognitive load, has the potential to be used as a tool not only to measure cognitive activity quantitatively, but also to detect cognitive impairment.

Functional brain networks (FBNs) are gaining increasing attention in computational neuroscience due to their ability to reveal dynamic interdependencies between brain regions. The dynamics of such networks during cognitive activity between stimulus and response using multi-channel electroencephalogram (EEG), recorded from 16 healthy human participants are explored in this research. Successive EEG segments of 500[Formula: see text]ms duration starting from the onset of cognitive stimulation have been used to analyze and understand the cognitive dynamics. The approach employs a combination of signal processing techniques, nonlinear statistical measures and graph-theoretical analysis. The efficacy of this approach in detecting and tracking cognitive load induced changes in EEG data is clearly demonstrated using graph metrics. It is revealed that most cognitive activity occurs within approximately 500[Formula: see text]ms of the stimulus presentation in addition to temporal variability in the FBNs. It is shown that mutual information (MI), a nonlinear measure, produces good correlations between the EEG channels thus enabling the construction of FBNs which are sensitive to cognitive load induced changes in EEG. Analyses of the dynamics of FBNs and the visualization approach reveal hard to detect subtle changes in cognitive function and hence may lead to a better understanding of cognitive processing in the brain. The techniques exploited have the potential to detect human cognitive dysfunction (impairments).

Visualizing the ERD/ERS phenomenon in terms of pole displacement is a novel approach. Although ERS/ERD has previously been demonstrated as reliable when applied to motor related tasks, it is believed to be the first time that it has been applied to investigate human cognitive phenomena such as attention and distraction. Results confirmed that distracted/non-distracted driving states can be identified using this approach supporting its applicability to cognition research.

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