MEG Source Imaging Using Multipolar Expansions
IPMI '99 Proceedings of the 16th International Conference on Information Processing in Medical Imaging
Unfolding the Cerebral Cortex Using Level Set Methods
SCALE-SPACE '99 Proceedings of the Second International Conference on Scale-Space Theories in Computer Vision
Hierarchical Bayesian Inference of Brain Activity
Neural Information Processing
A Distributed Spatio-temporal EEG/MEG Inverse Solver
MICCAI '08 Proceedings of the 11th international conference on Medical Image Computing and Computer-Assisted Intervention - Part I
Distribution-based minimum-norm estimation with multiple trials
Computers in Biology and Medicine
EEG/MEG source imaging: methods, challenges, and open issues
Computational Intelligence and Neuroscience - Neuromath: advanced methods for the estimation of human brain activity and connectivity
Improving M/EEG source localization with an inter-condition sparse prior
ISBI'09 Proceedings of the Sixth IEEE international conference on Symposium on Biomedical Imaging: From Nano to Macro
Dynamic imaging of cognitive impairment in nicotine-deprived subjects using simultaneous EEG/FMRI
ISBI'09 Proceedings of the Sixth IEEE international conference on Symposium on Biomedical Imaging: From Nano to Macro
MICCAI'07 Proceedings of the 10th international conference on Medical image computing and computer-assisted intervention
Multi-modal ICA exemplified on simultaneously measured MEG and EEG data
ICA'07 Proceedings of the 7th international conference on Independent component analysis and signal separation
Eeg-fmri fusion of paradigm-free activity using kalman filtering
Neural Computation
Computational Intelligence and Neuroscience - Special issue on processing of brain signals by using hemodynamic and neuroelectromagnetic modalities
3DIM'99 Proceedings of the 2nd international conference on 3-D digital imaging and modeling
A genetic algorithm for the topology correction of cortical surfaces
IPMI'05 Proceedings of the 19th international conference on Information Processing in Medical Imaging
Advances in Human-Computer Interaction - Special issue on Using Brain Waves to Control Computers and Machines
Computer Methods and Programs in Biomedicine
Journal of Cognitive Neuroscience
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We describe a comprehensive linear approach to the problem of imaging brain activity with high temporal as well as spatial resolution based on combining EEG and MEG data with anatomical constraints derived from MRI images. The "inverse problem" of estimating the distribution of dipole strengths over the cortical surface is highly underdetermined, even given closely spaced EEG and MEG recordings. We have obtained much better solutions to this problem by explicitly incorporating both local cortical orientation as well as spatial covariance of sources and sensors into our formulation. An explicit polygonal model of the cortical manifold is first constructed as follows: (1) slice data in three orthogonal planes of section (needle-shaped voxels) are combined with a linear deblurring technique to make a single high-resolution 3-D image (cubic voxels), (2) the image is recursively flood-filled to determine the topology of the gray-white matter border, and (3) the resulting continuous surface is refined by relaxing it against the original 3-D gray-scale image using a deformable template method, which is also used to computationally flatten the cortex for easier viewing. The explicit solution to an error minimization formulation of an optimal inverse linear operator (for a particular cortical manifold, sensor placement, noise and prior source covariance) gives rise to a compact expression that is practically computable for hundreds of sensors and thousands of sources. The inverse solution can then be weighted for a particular (averaged) event using the sensor covariance for that event. Model studies suggest that we may be able to localize multiple cortical sources with spatial resolution as good as PET with this technique, while retaining a much finer grained picture of activity over time.