title: Moment to moment variability in functional brain networks during cognitive activity in EEG data.
abstract: 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).
authors.affiliation: * Cognitive Neuro-Engineering & Computational Neuroscience Laboratory, School of Information Technology & Mathematical Sciences, University of South Australia, Mawson Lakes Campus, Adelaide, Australia.
authors.forename: Naga M
authors.initials: NM
authors.lastname: Dasari
authors.affiliation: * Cognitive Neuro-Engineering & Computational Neuroscience Laboratory, School of Information Technology & Mathematical Sciences, University of South Australia, Mawson Lakes Campus, Adelaide, Australia.
authors.forename: Nanda D
authors.initials: ND
authors.lastname: Nandagopal
authors.affiliation: † Department of Applied Mathematics and Computational Sciences, PSG College of Technology, Tamil Nadu, India.
authors.forename: Vijayalaxmi
authors.initials: V
authors.lastname: Ramasamy
authors.affiliation: * Cognitive Neuro-Engineering & Computational Neuroscience Laboratory, School of Information Technology & Mathematical Sciences, University of South Australia, Mawson Lakes Campus, Adelaide, Australia.
authors.forename: Bernadine
authors.initials: B
authors.lastname: Cocks
authors.affiliation: † Department of Applied Mathematics and Computational Sciences, PSG College of Technology, Tamil Nadu, India.
authors.forename: Bruce H
authors.initials: BH
authors.lastname: Thomas
authors.affiliation: * Cognitive Neuro-Engineering & Computational Neuroscience Laboratory, School of Information Technology & Mathematical Sciences, University of South Australia, Mawson Lakes Campus, Adelaide, Australia.
authors.forename: Nabaraj
authors.initials: N
authors.lastname: Dahal
authors.affiliation: ‡ Defence Science and Technology Group, Edinburgh, South Australia, Australia.
authors.forename: Paul
authors.initials: P
authors.lastname: Gaertner
mesh_terms: D000328:Adult; D001921:Brain / Q000502:physiology*; D001931:Brain Mapping / Q000379:methods; D003071:Cognition / Q000502:physiology*; D004569:Electroencephalography / Q000379:methods; D006801:Humans; D007257:Information Theory; D008875:Middle Aged; D009434:Neural Pathways / Q000502:physiology; D009483:Neuropsychological Tests; D017711:Nonlinear Dynamics; D013997:Time Factors; D055815:Young Adult
keywords: Functional brain network; brain network variability; cognition; graph metrics; moment-to-moment; mutual information