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Simulating and Reconstructing Neurodynamics with Epsilon-Automata Applied To Electroencephalography (EEG) Microstate Sequences

Research output: Chapter in Book/Report/Conference proceedingConference paperpeer-review

Chrystopher Nehaniv, Elena Antonova

Original languageEnglish
Title of host publicationSimulating and Reconstructing Neurodynamics with Epsilon-Automata Applied to Electroencephalography (EEG) Microstate Sequences.
Place of PublicationProceedings of the IEEE Symposium on Computational Intelligence, Cognitive Algorithms, Mind, and Brain (IEEE CCMB'17)
PublisherI E E E
Pages1753-1761
Volume2017
PublishedNov 2017

Publication series

NameIEEE Symposium Series on Computational Intelligence, 27 November - 1 December 2017, Honolulu, Hawaii, U.S.A.

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King's Authors

Abstract

We introduce new techniques to the analysis of
neural spatiotemporal dynamics via applying epsilon-machine reconstruction
to electroencephalography (EEG) microstate sequences.
Microstates are short duration quasi-stable states of the dynamically
changing electrical field topographies recorded via an
array of electrodes from the human scalp, and cluster into four
canonical classes. The sequence of microstates observed under
particular conditions can be considered an information source
with unknown underlying structure. Epsilon-machines are discrete
dynamical system automata with state-dependent probabilities on
different future observations (in this case the next measured EEG
microstate). They artificially reproduce underlying structure in
an optimally predictive manner as generative models exhibiting
dynamics emulating the behaviour of the source. Here we present
experiments using both simulations and empirical data supporting
the value of associating these discrete dynamical systems
with mental states (e.g. mind-wandering, focused attention, etc.)
and with clinical populations. The neurodynamics of mental
states and clinical populations can then be further characterized
by properties of these dynamical systems, including: i)
statistical complexity (determined by the number of states of
the corresponding epsilon-automaton); ii) entropy rate; iii) characteristic
sequence patterning (syntax, probabilistic grammars);
iv) duration, persistence and stability of dynamical patterns;
and v) algebraic measures such as Krohn-Rhodes complexity or
holonomy length of the decompositions of these. The potential
applications include the characterization of mental states in
neurodynamic terms for mental health diagnostics, well-being
interventions, human-machine interface, and others on both
subject-specific and group/population-level.

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