Explicit Modeling of Brain State Duration Using Hidden Semi Markov Models in EEG Data
We 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, o...
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| Format: | Article |
| Language: | English |
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IEEE
2024-01-01
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| Series: | IEEE Access |
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| Online Access: | https://ieeexplore.ieee.org/document/10400476/ |
| _version_ | 1797346333320806400 |
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| author | Nelson J. Trujillo-Barreto David Araya Galvez Aland Astudillo Wael El-Deredy |
| author_facet | Nelson J. Trujillo-Barreto David Araya Galvez Aland Astudillo Wael El-Deredy |
| author_sort | Nelson J. Trujillo-Barreto |
| collection | DOAJ |
| description | We 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. |
| first_indexed | 2024-03-08T11:31:34Z |
| format | Article |
| id | doaj.art-f11e2c77a14c4f9bb61c14b4953e1531 |
| institution | Directory Open Access Journal |
| issn | 2169-3536 |
| language | English |
| last_indexed | 2024-03-08T11:31:34Z |
| publishDate | 2024-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj.art-f11e2c77a14c4f9bb61c14b4953e15312024-01-26T00:01:21ZengIEEEIEEE Access2169-35362024-01-0112123351235510.1109/ACCESS.2024.335471110400476Explicit Modeling of Brain State Duration Using Hidden Semi Markov Models in EEG DataNelson J. Trujillo-Barreto0https://orcid.org/0000-0001-6581-7503David Araya Galvez1https://orcid.org/0009-0006-6254-4339Aland Astudillo2https://orcid.org/0009-0008-8672-3168Wael El-Deredy3https://orcid.org/0000-0002-9822-1092Faculty of Biology, Medicine and Health, School of Health Sciences, The University of Manchester, Manchester, U.K.Facultad de Ingeniería, Instituto de Tecnología para la Innovación en Salud y Bienestar (ITISB), Universidad Andrés Bello, Viña del Mar, ChileNICM Health Research Institute, Western Sydney University, Penrith, NSW, AustraliaCentro de Investigación y Desarrollo en Ingeniería en Salud, Universidad de Valparaíso, Valparaíso, ChileWe 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.https://ieeexplore.ieee.org/document/10400476/Brain statehidden semi Markov modelstate durationEEG |
| spellingShingle | Nelson J. Trujillo-Barreto David Araya Galvez Aland Astudillo Wael El-Deredy Explicit Modeling of Brain State Duration Using Hidden Semi Markov Models in EEG Data IEEE Access Brain state hidden semi Markov model state duration EEG |
| title | Explicit Modeling of Brain State Duration Using Hidden Semi Markov Models in EEG Data |
| title_full | Explicit Modeling of Brain State Duration Using Hidden Semi Markov Models in EEG Data |
| title_fullStr | Explicit Modeling of Brain State Duration Using Hidden Semi Markov Models in EEG Data |
| title_full_unstemmed | Explicit Modeling of Brain State Duration Using Hidden Semi Markov Models in EEG Data |
| title_short | Explicit Modeling of Brain State Duration Using Hidden Semi Markov Models in EEG Data |
| title_sort | explicit modeling of brain state duration using hidden semi markov models in eeg data |
| topic | Brain state hidden semi Markov model state duration EEG |
| url | https://ieeexplore.ieee.org/document/10400476/ |
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