UH Biocomputation Group - Adaptation and Self-Organizing Systemshttp://biocomputation.herts.ac.uk/2022-10-18T19:27:12+01:00Novel Computational Methods for Predicting Transitions in Spatiotemporal Neurodynamics between Attention and Mind-wandering2022-10-18T19:27:12+01:002022-10-18T19:27:12+01:00David Haydocktag:biocomputation.herts.ac.uk,2022-10-18:/2022/10/18/novel-computational-methods-for-predicting-transitions-in-spatiotemporal-neurodynamics-between-attention-and-mind-wandering.html<p class="first last">David Haydock's Journal Club session where he will talk about his recent work on "Novel Computational Methods for Predicting Transitions in Spatiotemporal Neurodynamics between Attention and Mind-wandering"</p>
<p>This week on Journal Club session David Haydock will talk about his recent work
on "Novel Computational Methods for Predicting Transitions in Spatiotemporal
Neurodynamics between Attention and Mind-wandering".</p>
<hr class="docutils" />
<p>Abstract of related work:</p>
<p>We introduce new techniques to the analysis of neural spatiotemporal
dynamics via applying -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. -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 -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.</p>
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<p>Papers:</p>
<ul class="simple">
<li>C. Nehaniv, E. Antonova, <a class="reference external" href="https://doi.org/10.1109/SSCI.2017.8285438">"Simulating and Reconstructing Neurodynamics with Epsilon-Automata Applied to Electroencephalography (EEG) Microstate Sequences"</a>, 2017, 2017 IEEE Symposium Series on Computational Intelligence (SSCI), 1--9</li>
</ul>
<p><strong>Date:</strong> 2022/10/21 <br />
<strong>Time:</strong> 14:00 <br />
<strong>Location</strong>: online</p>
Simulating and Reconstructing Neurodynamics with Epsilon-Automata Applied to Electroencephalography (EEG) Microstate Sequences2021-11-10T10:37:09+00:002021-11-10T10:37:09+00:00David Haydocktag:biocomputation.herts.ac.uk,2021-11-10:/2021/11/10/simulating-and-reconstructing-neurodynamics-with-epsilon-automata-applied-to-electroencephalography-eeg-microstate-sequences.html<p class="first last">David Haydock's Journal Club session where he will talk about a paper "Simulating and Reconstructing Neurodynamics with Epsilon-Automata Applied to Electroencephalography (EEG) Microstate Sequences"</p>
<p>This week on Journal Club session David Haydock will talk about a paper "Simulating and Reconstructing Neurodynamics with Epsilon-Automata Applied to Electroencephalography (EEG) Microstate Sequences".</p>
<hr class="docutils" />
<p>We introduce new techniques to the analysis of neural spatiotemporal
dynamics via applying -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. -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 -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.</p>
<div class="line-block">
<div class="line"><br /></div>
</div>
<p>Papers:</p>
<ul class="simple">
<li>C. Nehaniv, E. Antonova, <a class="reference external" href="https://doi.org/10.1109/SSCI.2017.8285438">"Simulating and Reconstructing Neurodynamics with Epsilon-Automata Applied to Electroencephalography (EEG) Microstate Sequences"</a>, 2017, 2017 IEEE Symposium Series on Computational Intelligence (SSCI), 1--9</li>
</ul>
<p><strong>Date:</strong> 2021/11/12 <br />
<strong>Time:</strong> 14:00 <br />
<strong>Location</strong>: online</p>