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.

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Understanding the secrets underlying the brain functioning would be the noble achievement of this era. Learning how brain learns would be the milestone to guide the researchers of artificial intelligence, neurology and psychology. With the advent of "Integrate and Fire" model of neuron proposed about a hundred years ago, the brain research has picked up its pace in the study of different aspects of brain functionality. Many cogni tive architectures have been proposed with an aim of simulating and understanding human cognition. On the other hand, many technologies have emerged that can measure the parameters of the brain activity. Among them, Electroencephalogram (EEG) stands as a reliable tool in the study of brain functioning.Simplified wireless EEGs are readily available now which can send data recorded by its electrodes to a computer for further processing. We have chosen this tool to detect different aspects of cognition and to predict the brain functioning behind it. A lot of studies from the past two decades have already revealed varying EEG patterns related to cognition. In this paper, we have proposed to extract different features from visual, tactile, auditory and psychomotor stimuli to work on different cognitive aspects such as memory, emotion, arousal, fatigue and distraction and to investigate its affect on the EEG. A methodology to model cognitive functions by relating the varying event related potential, brain waves, spectral density and latency in EEG outcomes are then related with the stimuli features to predict the cognitive state of mind.

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.