60 research outputs found
EEG inverse problem solution with minimal influence of the conductivity
In this paper, we propose a novel method that improves the accuracy of the estimation of neural electrical dipoles when solving the EEG inverse problem. A spherical head model is used where we limit the influence of the unknown conductivity brain-skull ratio on the inverse problem.We redefine the cost function that is used in the EEG problem where only useful information is used as input in the inverse problem. In contrast to previous approaches, weighting factors are used where the electrodes are strategically chosen so to reduce the error made on EEG dipole source localization. The proposed method enhances the source localization accuracy from approximately 9mm to 1mm for dipoles near the edge and from 2.1mm to 0.4mm for dipoles near the center of the brain
An inversion method based on random sampling for real-time MEG neuroimaging
The MagnetoEncephaloGraphy (MEG) is a non-invasive neuroimaging technique with a high temporal resolution which can be successfully used in real-time applications, such as brain-computer interface training or neurofeedback rehabilitation.
The localization of the active area of the brain from MEG data results in a highly ill-posed and ill-conditioned inverse problem that requires fast and efficient inversion methods to be solved. In this paper we use an inversion method based on random spatial sampling to solve the MEG inverse problem. The method is fast, efficient and has a low computational load. The numerical tests show that the method can produce accurate map of the electric activity inside the brain even in case of deep neural sources
Inverse Modeling for MEG/EEG data
We provide an overview of the state-of-the-art for mathematical methods that
are used to reconstruct brain activity from neurophysiological data. After a
brief introduction on the mathematics of the forward problem, we discuss
standard and recently proposed regularization methods, as well as Monte Carlo
techniques for Bayesian inference. We classify the inverse methods based on the
underlying source model, and discuss advantages and disadvantages. Finally we
describe an application to the pre-surgical evaluation of epileptic patients.Comment: 15 pages, 1 figur
Localization of abnormal EEG sources using blind source separation partially constrained by the locations of known sources
Electroencephalogram (EEG) source localization
requires a solution to an ill-posed inverse problem. The additional
challenge is to solve this problem in the context of multiple moving
sources. An effective and simple technique for both separation
and localization of EEG sources is therefore proposed by incorporating
an algorithmically coupled blind source separation (BSS)
approach. The method relies upon having a priori knowledge of the
locations of a subset of the sources. The cost function of the BSS
algorithm is constrained by this information, and the unknown
sources are iteratively calculated. An important application of this
method is to localize abnormal sources, which, for example, cause
changes in attention, movement, and behavior. In this application,
the Alpha rhythm was considered as the known sources. Simulation
studies are presented to support the potential of the approach
in terms of source localization
Monitoring cortical excitability during repetitive transcranial magnetic stimulation in children with ADHD: a single-blind, sham-controlled TMS-EEG study
Background: Repetitive transcranial magnetic stimulation (rTMS) allows non-invasive stimulation of the human brain. However, no suitable marker has yet been established to monitor the immediate rTMS effects on cortical areas in children.
Objective: TMS-evoked EEG potentials (TEPs) could present a well-suited marker for real-time monitoring. Monitoring is particularly important in children where only few data about rTMS effects and safety are currently available.
Methods: In a single-blind sham-controlled study, twenty-five school-aged children with ADHD received subthreshold 1 Hz-rTMS to the primary motor cortex. The TMS-evoked N100 was measured by 64-channel-EEG pre, during and post rTMS, and compared to sham stimulation as an intraindividual control condition.
Results: TMS-evoked N100 amplitude decreased during 1 Hz-rTMS and, at the group level, reached a stable plateau after approximately 500 pulses. N100 amplitude to supra-threshold single pulses post rTMS confirmed the amplitude reduction in comparison to the pre-rTMS level while sham stimulation had no influence. EEG source analysis indicated that the TMS-evoked N100 change reflected rTMS effects in the stimulated motor cortex. Amplitude changes in TMS-evoked N100 and MEPs (pre versus post 1 Hz-rTMS) correlated significantly, but this correlation was also found for pre versus post sham stimulation.
Conclusion: The TMS-evoked N100 represents a promising candidate marker to monitor rTMS effects on cortical excitability in children with ADHD. TMS-evoked N100 can be employed to monitor real-time effects of TMS for subthreshold intensities. Though TMS-evoked N100 was a more sensitive parameter for rTMS-specific changes than MEPs in our sample, further studies are necessary to demonstrate whether clinical rTMS effects can be predicted from rTMS-induced changes in TMS-evoked N100 amplitude and to clarify the relationship between rTMS-induced changes in TMS-evoked N100 and MEP amplitudes. The TMS-evoked N100 amplitude reduction after 1 Hz-rTMS could either reflect a globally decreased cortical response to the TMS pulse or a specific decrease in inhibition
Sequential Monte Carlo samplers for semilinear inverse problems and application to magnetoencephalography
We discuss the use of a recent class of sequential Monte Carlo methods for
solving inverse problems characterized by a semi-linear structure, i.e. where
the data depend linearly on a subset of variables and nonlinearly on the
remaining ones. In this type of problems, under proper Gaussian assumptions one
can marginalize the linear variables. This means that the Monte Carlo procedure
needs only to be applied to the nonlinear variables, while the linear ones can
be treated analytically; as a result, the Monte Carlo variance and/or the
computational cost decrease. We use this approach to solve the inverse problem
of magnetoencephalography, with a multi-dipole model for the sources. Here,
data depend nonlinearly on the number of sources and their locations, and
depend linearly on their current vectors. The semi-analytic approach enables us
to estimate the number of dipoles and their location from a whole time-series,
rather than a single time point, while keeping a low computational cost.Comment: 26 pages, 6 figure
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