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Novel Computational Methods for Predicting Transitions in Spatiotemporal Neurodynamics between Attention and Mind-wandering

Computer Science

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Dr E Antonova No more applications being accepted Self-Funded PhD Students Only

About the Project

The project will aim to build an explorative and predictive model of the brain that is sensitive to the transitions between sustained attention and mind-wandering states. Such a predictive model potentially has applications in tracking attention during critical tasks as well as being of medical and diagnostic relevance. Towards this goal, novel methods for characterizing and predicting the spatio-temporal dynamics of the brain at two complementary levels at two complimentary resolution levels will be developed:
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.

Funding Notes

Brunel offers a number of funding options to research students that help cover the cost of their tuition fees, contribute to living expenses or both. See more information here: Recently the UK Government made available the Doctoral Student Loans of up to £25,000 for UK and EU students and there is some funding available through the Research Councils. Many of our international students benefit from funding provided by their governments or employers. Brunel alumni enjoy tuition fee discounts of 15%.)


Nehaniv CL, Antonova E, Simulating and Reconstructing Neurodynamics with Epsilon-Automata Applied to Electroencephalography (EEG) Microstate Sequences, IEEE Symposium on Computational Intelligence, Cognitive Algorithms, Mind, and Brain (IEEE CCMB'17), at IEEE Symposium Series on Computational Intelligence, 27 November - 1 December 2017, Honolulu, Hawaii, U.S.A., pp. 1753-1761, IEEE Press, 2017.

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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