About the Project
Level 1. Electroencephalograph (EEG) microstates, which are short quasi-stable topographies of brain electrical activity as measured at the scalp, with high temporal resolution on the order of 80-120 milliseconds.
Level 2. Functional Magnetic Resonance Imaging (fMRI) functional connectivity maps, which reveal networks of blood-oxygen-level-dependent activation in distributed brain areas at a slower time scale, on the order of seconds, with high spatial resolution.
The methods developed will serve directly in (1) predicting the transitions between attention
and mind-wandering in terms of neurodynamics, but will also (2) contribute to the general
foundation for using neuroscience algorithmics in human-machine interface, operator attention monitoring, and (3) may underpin better understanding and prediction of the neurodynamics in a variety of human activities.
The project will be suitable for an individual with an interest in cognitive neuroscience and neuroimaging, who has strong programming skills (i.e. C++, Python, Matlab). The background in computer science or computational neuroscience are most relevant. Previous experience of EEG and/or fMRI data analysis is desirable.
Nehaniv CL, Rhodes JL, Egri-Nagy A, Dini P, Rothstein Morris E, Horváth G, Karimi F, Schreckling D, Schilstra MJ,Symmetry Structure in Discrete Models of Biochemical Systems: Natural Subsystems and the Weak Control Hierarchy in a New Model of Computation Driven by Interactions, Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences, 373: 20140223, 2015.
Britz J, Van De Ville D, Michel CM. BOLD correlates of EEG topography reveal rapid resting-state network dynamics. NeuroImage. 2010; 52:1162–1170.
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