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Abstract
We introduce new techniques to the analysis of
neural spatiotemporal dynamics via applying epsilonmachine reconstruction
to electroencephalography (EEG) microstate sequences.
Microstates are short duration quasistable 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. Epsilonmachines are discrete
dynamical system automata with statedependent 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. mindwandering, 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 epsilonautomaton); ii) entropy rate; iii) characteristic
sequence patterning (syntax, probabilistic grammars);
iv) duration, persistence and stability of dynamical patterns;
and v) algebraic measures such as KrohnRhodes 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, wellbeing
interventions, humanmachine interface, and others on both
subjectspecific and group/populationlevel.
neural spatiotemporal dynamics via applying epsilonmachine reconstruction
to electroencephalography (EEG) microstate sequences.
Microstates are short duration quasistable 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. Epsilonmachines are discrete
dynamical system automata with statedependent 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. mindwandering, 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 epsilonautomaton); ii) entropy rate; iii) characteristic
sequence patterning (syntax, probabilistic grammars);
iv) duration, persistence and stability of dynamical patterns;
and v) algebraic measures such as KrohnRhodes 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, wellbeing
interventions, humanmachine interface, and others on both
subjectspecific and group/populationlevel.
Original language  English 

Title of host publication  Simulating and Reconstructing Neurodynamics with EpsilonAutomata Applied to Electroencephalography (EEG) Microstate Sequences. 
Place of Publication  Proceedings of the IEEE Symposium on Computational Intelligence, Cognitive Algorithms, Mind, and Brain (IEEE CCMB'17) 
Publisher  I E E E 
Pages  17531761 
Volume  2017 
Publication status  Published  Nov 2017 
Publication series
Name  IEEE Symposium Series on Computational Intelligence, 27 November  1 December 2017, Honolulu, Hawaii, U.S.A. 

Keywords
 EEG microstates
 Meditation
 Epsilonautomata
 Neuroimaging
 EEG methods
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 1 Finished

Decoding the language of 'now': EEG microstates in experienced meditators, from letters to grammar.
1/09/2017 → 31/08/2019
Project: Research