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EEG multi-mode oscillatory brain state allocation using switching spectral gaussian processes

dc.contributor.authorPrieur-Coloma, Yunier
dc.contributor.authorTorres, Felipe A.
dc.contributor.authorTrujillo-Barreto, Nelson J.
dc.contributor.authorEl-Deredy, Wael
dc.date.accessioned2025-07-04T17:56:43Z
dc.date.available2025-07-04T17:56:43Z
dc.date.issued2025
dc.identifier.urihttp://repositorio.ucm.cl/handle/ucm/6177
dc.description.abstractWe propose a new model for the non-stationary brain state allocation problem from electroencephalography (EEG) data, based on spectral features and their interaction. Spontaneous EEG data are modeled as continuous Gaussian Processes (GPs) emissions governed by discrete states, represented by a hidden semi-Markov model, that switch in time (HsMM-SGP). The GPs are defined by multivariate spectral kernels, covariance functions parameterized in the frequency domain. The multivariate spectral kernels describe oscillatory modes at specific frequencies and their interactions across channels, encapsulating periodicity, amplitude, and spread. Multivariate spectral kernels enable the GPs to represent temporal patterns with fine-grained frequency-specific structures and interactions, a unique spectral “fingerprint” per state, making it particularly suited for capturing non-stationary oscillatory behaviour in the neural time series. The model parameters were estimated using the Expectation-Maximization approach. The inference scheme was validated on data generated from the HsMM-SGP generative model to evaluate the accuracy in recovering the ground truth parameters. Next, we generated time-series from a metastable connectome-connected whole brain network to demonstrate the HsMM-SGP’s capability to infer meaningful oscillatory modes that reflect the changes in the underlying dynamics due to varying structural connectivity parameters. Finally, a practical application of the HsMM-SGP is illustrated using EEG data from a healthy control and an AD patient. We show that the inferred brain states exhibit distinct spectral properties across both conditions, with the AD states marked slower frequencies. We conclude that the proposed HsMM-SGP offers a method for estimating physiologically meaningful dynamical brain states.es_CL
dc.language.isoenes_CL
dc.rightsAtribución-NoComercial-SinDerivadas 3.0 Chile*
dc.rights.urihttp://creativecommons.org/licenses/by-nc-nd/3.0/cl/*
dc.sourceIEEE Access, 13, 56053-56066es_CL
dc.subjectKerneles_CL
dc.subjectBrain modelinges_CL
dc.subjectGaussian processeses_CL
dc.subjectElectroencephalographyes_CL
dc.subjectTime series analysises_CL
dc.subjectResource managementes_CL
dc.subjectData modelses_CL
dc.subjectSwitcheses_CL
dc.subjectPhysiologyes_CL
dc.subjectHidden Markov modelses_CL
dc.titleEEG multi-mode oscillatory brain state allocation using switching spectral gaussian processeses_CL
dc.typeArticlees_CL
dc.ucm.facultadFacultad de Ciencias de la Ingenieríaes_CL
dc.ucm.indexacionScopuses_CL
dc.ucm.indexacionIsies_CL
dc.ucm.uriieeexplore.ieee.org/document/10937758es_CL
dc.ucm.doidoi.org/10.1109/ACCESS.2025.3554137es_CL


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