Explicit Modeling of Brain State Duration Using Hidden Semi Markov Models in EEG Data

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dc.contributor.authorTrujillo-Barreto, Nelson J.
dc.contributor.authorGalvez, David Araya
dc.contributor.authorAstudillo, Aland
dc.contributor.authorEl-Deredy, Wael
dc.date.accessioned2025-01-22T15:17:54Z
dc.date.available2025-01-22T15:17:54Z
dc.date.issued2024
dc.descriptionIndexación: Scopus
dc.description.abstractWe consider the detection and characterization of brain state transitions based on ongoing electroencephalography (EEG). Here, a brain state represents a specific brain dynamical regime or mode of operation that produces a characteristic quasi-stable pattern of activity at the topography, sources, or network levels. These states and their transitions over time can reflect fundamental computational properties of the brain, shaping human behavior and brain function. The hidden Markov model (HMM) has emerged as a useful tool for uncovering the hidden dynamics of brain state transitions based on observed data. However, the limitations of the Geometric distribution of states' durations (dwell times) implicit in the standard HMM, make it sub-optimal for modeling brain states in EEG. We propose using hidden semi Markov models (HSMM), a generalization of HMM that allows modeling the brain states duration distributions explicitly. We present a Bayesian formulation of HSMM and use the variational Bayes framework to efficiently estimate the HSMM parameters, the number of brain states, and select among candidate brain state duration distributions. We assess HSMM performance against HMM on simulated data and demonstrate that the accurate modeling of state durations is paramount for making reliable inference when the task at hand requires accurate model predictions. Finally, we use actual resting-state EEG data to illustrate the benefits of the approach in practice. We demonstrate that the possibility of modeling brain state durations explicitly provides a new way for investigating the nature of the neural dynamics that generated the EEG data. © 2013 IEEE.
dc.description.urihttps://ieeexplore-ieee-org.recursosbiblioteca.unab.cl/document/10400476
dc.identifier.citationIEEE . Volume 12, Pages 12335 - 12355. 2024
dc.identifier.doi10.1109/ACCESS.2024.3354711
dc.identifier.issn2169-3536
dc.identifier.urihttps://repositorio.unab.cl/handle/ria/63194
dc.language.isoen
dc.publisherInstitute of Electrical and Electronics Engineers Inc.
dc.rights.licenseAttribution 4.0 International Deed (CC BY 4.0)
dc.rights.urihttps://creativecommons.org/licenses/by/4.0/
dc.subjectBrain State
dc.subjectEEG
dc.subjectHidden Semi Markov Model
dc.subjectState Duration
dc.titleExplicit Modeling of Brain State Duration Using Hidden Semi Markov Models in EEG Data
dc.typeArtículo
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