11 research outputs found
Fast Approximation of EEG Forward Problem and Application to Tissue Conductivity Estimation
Bioelectric source analysis in the human brain from scalp
electroencephalography (EEG) signals is sensitive to the conductivity of the
different head tissues. Conductivity values are subject dependent, so
non-invasive methods for conductivity estimation are necessary to fine tune the
EEG models. To do so, the EEG forward problem solution (so-called lead field
matrix) must be computed for a large number of conductivity configurations.
Computing one lead field requires a matrix inversion which is computationally
intensive for realistic head models. Thus, the required time for computing a
large number of lead fields can become impractical. In this work, we propose to
approximate the lead field matrix for a set of conductivity configurations,
using the exact solution only for a small set of basis points in the
conductivity space. Our approach accelerates the computing time, while
controlling the approximation error. Our method is tested for brain and skull
conductivity estimation , with simulated and measured EEG data, corresponding
to evoked somato-sensory potentials. This test demonstrates that the used
approximation does not introduce any bias and runs significantly faster than if
exact lead field were to be computed.Comment: Copyright (c) 2019 IEEE. Personal use of this material is permitted.
However, permission to use this material for any other purposes must be
obtained from the IEEE by sending a request to [email protected]
Detecting large-scale networks in the human brain using high-density electroencephalography
Highâdensity electroencephalography (hdEEG) is an emerging brain imaging technique that can be used to investigate fast dynamics of electrical activity in the healthy and the diseased human brain. Its applications are however currently limited by a number of methodological issues, among which the difficulty in obtaining accurate source localizations. In particular, these issues have so far prevented EEG studies from reporting brain networks similar to those previously detected by functional magnetic resonance imaging (fMRI). Here, we report for the first time a robust detection of brain networks from resting state (256âchannel) hdEEG recordings. Specifically, we obtained 14 networks previously described in fMRI studies by means of realistic 12âlayer head models and exact lowâresolution brain electromagnetic tomography (eLORETA) source localization, together with independent component analysis (ICA) for functional connectivity analysis. Our analyses revealed three important methodological aspects. First, brain network reconstruction can be improved by performing source localization using the gray matter as source space, instead of the whole brain. Second, conducting EEG connectivity analyses in individual space rather than on concatenated datasets may be preferable, as it permits to incorporate realistic information on head modeling and electrode positioning. Third, the use of a wide frequency band leads to an unbiased and generally accurate reconstruction of several network maps, whereas filtering data in a narrow frequency band may enhance the detection of specific networks and penalize that of others. We hope that our methodological work will contribute to rise of hdEEG as a powerful tool for brain research.Funding Information:
- Chinese Scholarship Council. Grant Number: 201306180008
- Swiss National Science Foundation. Grant Number: 320030_146531 and P1EZP3_165207
- Seventh Framework Programme European Commission. Grant Number: PCIG12â334039
- KU Leuven Special Research Fund. Grant Number: C16/15/070
Research Foundation Flanders (FWO). Grant Number: G0F76.16N and G0936.16