Camilo Lamus

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MEG/EEG are non-invasive imaging techniques that record brain activity with high temporal resolution. However, estimation of brain source currents from surface recordings requires solving an ill-conditioned inverse problem. Converging lines of evidence in neuroscience, from neuronal network models to resting-state imaging and neurophysiology, suggest that(More)
It has been long appreciated that anesthetic drugs induce stereotyped changes in electroencephalogram (EEG), but the relationships between the EEG and underlying brain function remain poorly understood. Functional imaging methods including positron emission tomography (PET) and functional magnetic resonance imaging (fMRI), have become important tools for(More)
Magnetoencephalography (MEG) is an important non-invasive method for studying activity within the human brain. Source localization methods can be used to estimate spatiotemporal activity from MEG measurements with high temporal resolution, but the spatial resolution of these estimates is poor due to the ill-posed nature of the MEG inverse problem. Recent(More)
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(More)
Dynamic estimation methods based on linear state-space models have been applied to the inverse problem of magnetoencephalography (MEG), and can improve source localization compared with static methods by incorporating temporal continuity as a constraint. The efficacy of these methods is influenced by how well the state-space model approximates the dynamics(More)
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(More)
We develop a state space approach to multimodal integration of simultaneously recorded EEG and fMRI. The EEG is represented with a distributed current source model using realistic MRI-based forward models, whose temporal evolution is governed by a linear state space model. The fMRI signal is similarly modeled by a linear state space model describing the(More)
  • Babadi, Behtash, +10 authors Patrick L. Purdona
  • 2014
Magnetoencephalography (MEG) is an important non-invasive method for studying activity within the human brain. Source localization methods can be used to estimate spatiotemporal activity from MEG measurements with high temporal resolution, but the spatial resolution of these estimates is poor due to the ill-posed nature of the MEG inverse problem. Recent(More)
MEG and EEG are noninvasive functional neuroimaging techniques that provide recordings of brain activity with high temporal resolution, and thus provide a unique window to study fast time-scale neural dynamics in humans. However, the accuracy of brain activity estimates resulting from these data is limited mainly because 1) the number of sensors is much(More)
OBJECTIVE Electroencephalography (EEG) and magnetoencephalography (MEG) non-invasively record scalp electromagnetic fields generated by cerebral currents, revealing millisecond-level brain dynamics useful for neuroscience and clinical applications. Estimating the currents that generate these fields, i.e., source localization, is an ill-conditioned inverse(More)
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