David Araya

(2019) PhD. (c) Biophysics and Computational Biology, Universidad de Valparaíso, Chile
Thesis project: Brain dynamics modeling as a hybrid system
We consider the detection of brain state transitions based on ongoing EEG/MEG data, from the perspective of Hybrid System Theory. Here a brain state signifies a characteristic quasi-stationary pattern of activity at topography, sources or network levels. These patterns can be interpreted as fingerprints left on the data by a specific underlying brain dynamical regime or mode of operation. The idea is that those characteristic states and the way they are explored by the brain over time (i.e. their transitions dynamics and durations) can reflect fundamental computational properties of the brain, being present in human behaviour and Neuroscience data. Furthermore, such state dependent fluctuations could be altered in brain disorders and thus could be used as markers for disease or disease progression. It is therefore important when modelling brain state allocations and transitions that the model captures accurately the number of states, their transition probabilities and their duration or occupancy time. We capitalise on recent advances and ideas from Hybrid System theory and propose that using Hidden Semi Markov Models (HSMM), where each state is associated with an explicit duration model, is a more accurate way of representing brain state switching, if these models were to be used to make actual interpretations of those states.
(2004) Electrical Engineering, Universidad de Chile, Chile
Areas of Interest
– Cognitive Neuroscience
– Computational Neuroscience
– Brain dynamics modeling
– Biomedical Signal processing
– Electroencephalography EEG
– Brain computer interface
– Neurofeedback
– Consciousness
– Machine Learning

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