RESUMO
Phase-amplitude modulation is a form of cross frequency coupling where the phase of one frequency influences the amplitude of another higher frequency. It has been observed in neurophysiological recordings during sensory, motor, and cognitive tasks, as well as during general anesthesia. In this paper, we describe a novel beamforming procedure to improve estimation of phase-amplitude modulation. We apply this method to 64-channel EEG data recorded during propofol general anesthesia. The method improves the sensitivity of phase-amplitude analyses, and can be applied to a variety of multi-channel neuroscience data where phase-amplitude modulation is present.
Assuntos
Anestesia Geral/métodos , Encéfalo/patologia , Eletroencefalografia/métodos , Neurofisiologia/métodos , Processamento de Sinais Assistido por Computador , Algoritmos , Encéfalo/efeitos dos fármacos , Cognição , Eletrodos , Análise de Fourier , Humanos , Modelos Estatísticos , Propofol/administração & dosagem , Análise de Regressão , SoftwareRESUMO
Recent dynamic source localization algorithms for the Magnetoencephalographic inverse problem use cortical spatio-temporal dynamics to enhance the quality of the estimation. However, these methods suffer from high computational complexity due to the large number of sources that must be estimated. In this work, we introduce a fast iterative greedy algorithm incorporating the class of subspace pursuit algorithms for sparse source localization. The algorithm employs a reduced order state-space model resulting in significant computational savings. Simulation studies on MEG source localization reveal substantial gains provided by the proposed method over the widely used minimum-norm estimate, in terms of localization accuracy, with a negligible increase in computational complexity.
Assuntos
Algoritmos , Magnetoencefalografia/métodos , Processamento de Sinais Assistido por Computador , Teorema de Bayes , Encéfalo/patologia , Mapeamento Encefálico/métodos , Interfaces Cérebro-Computador , Humanos , Modelos Estatísticos , Razão Sinal-Ruído , Software , Fatores de TempoRESUMO
Cortical activity can be estimated from electroencephalogram (EEG) or magnetoencephalogram (MEG) data by solving an ill-conditioned inverse problem that is regularized using neuroanatomical, computational, and dynamic constraints. Recent methods have incorporated spatio-temporal dynamics into the inverse problem framework. In this approach, spatio-temporal interactions between neighboring sources enforce a form of spatial smoothing that enhances source localization quality. However, spatial smoothing could also occur by way of correlations within the state noise process that drives the underlying dynamic model. Estimating the spatial covariance structure of this state noise is challenging, particularly in EEG and MEG data where the number of underlying sources is far greater than the number of sensors. However, the EEG/MEG data are sparse compared to the large number of sources, and thus sparse constraints could be used to simplify the form of the state noise spatial covariance. In this work, we introduce an empirically tailored basis to represent the spatial covariance structure within the state noise processes of a cortical dynamic model for EEG source localization. We augment the method presented in Lamus, et al. (2011) to allow for sparsity enforcing priors on the covariance parameters. Simulation studies as well as analysis of real data reveal significant gains in the source localization performance over existing algorithms.
