Naga Dasari

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Accurately identifying cognitive activity from multichannel EEG data continues to be a challenging task for cognition researchers. Although brain impairments such as epilepsy and ADHD tend to display relatively easy to identify EEG data features, delineating clear patterns of normal cognitive activity within the healthy brain has not yet been satisfactorily(More)
Identifying the integrative aspects of brain structure and function, specifically how the connections and interactions among neuronal elements (neurons, brain regions) result in cognition and behavior, is one of the last great frontiers for scientific research. Unraveling the activity of the brain’s billions of neurons and how they combine to form(More)
OBJECTIVE The objective of our current study was to look for the EEG correlates that can reveal the engaged state of the brain while undertaking cognitive tasks. Specifically, we aimed to identify EEG features that could detect audio distraction during simulated driving. APPROACH Time varying autoregressive (TVAR) analysis using Kalman smoother was(More)
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(More)
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