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Analytic sensing: a new technique for EEG source imaging

English title Analytic sensing: a new technique for EEG source imaging
Applicant Van De Ville Dimitri
Number 119812
Funding scheme Project funding (Div. I-III)
Research institution Service de Radiologie Hôpitaux Universitaires de Genève
Institution of higher education University of Geneva - GE
Main discipline Information Technology
Start/End 01.05.2008 - 30.04.2011
Approved amount 165'560.00
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All Disciplines (2)

Discipline
Information Technology
Neurophysiology and Brain Research

Keywords (7)

inverse problems; EEG; source localization; annihilating filters; electric neuroimaging; epilepsy; evoked response potentials

Lay Summary (English)

Lead
Lay summary
In this project, we propose new theoretical framework that will lead to a non-iterative technique for EEG source imaging. We designate our method as “analytic sensing”, since the key contribution is to apply analytic sensors (functions with vanishing Laplacian in some domain) that sense the influence of the source distribution in a specific region. The approach can be applied for multi-pole or multi-dipole source models, bringing together several attractive features: (1) the non-linear (dipole positions) and linear (dipolar moments) estimation steps are decoupled; (2) the non-linear estimation is direct (non-iterative) and fast; (3) no forward model is needed, while the scalp surface can be non-spherical; (4) the method can be spatially selective to only incorporate the influence of sources in a desired region-of-interest.
Direct link to Lay Summary Last update: 21.02.2013

Responsible applicant and co-applicants

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Associated projects

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153614 Ultrasound Tomography for Breast Cancer Detection and Breast Screening: Signal Processing Methods, Algorithms and Experiments 01.05.2014 Project funding (Div. I-III)
132808 Ultrasound Tomography for Breast Cancer Detection and Breast Screening: Signal Processing Methods, Algorithms and Experiments 01.11.2010 Project funding (Div. I-III)

Abstract

Imaging the functioning of the human brain is an important task in neurosciences and neurology. For that purpose, positron emission tomography (PET) and functional magnetic resonance imaging (fMRI) are playing an important role and provide good spatial resolution. However, to access the temporal properties of the brain circuits, electro- and magneto-encephalography (EEG, MEG) are predominant since they allow measuring signals down to millisecond resolution. Mapping back the measured signal on the scalp surface to the source configuration inside the brain is known as “source imaging”. Unfortunately, the underlying electromagnetic inverse problem is ill posed; i.e., an infinity of different source configurations can explain the same scalp potential [He1853]. Therefore, additional assumptions are required to make the solution unique. For this purpose, the various methods available are putting forward different hypotheses about the source model (single-dipole, multi-dipole, or distributed) and its properties (e.g., smoothness). In this project, we propose new theoretical framework that should lead to a non-iterative technique for EEG source imaging. We designate our method as “analytic sensing”, since the key contribution is to apply analytic sensors (functions with vanishing Laplacian in some domain) that sense the influence of the source distribution in a specific region. The approach can be applied for multi-pole or multi-dipole source models, bringing together several attractive features: (1) the non-linear (dipole positions) and linear (dipolar moments) estimation steps are decoupled; (2) the non-linear estimation is direct (non-iterative) and fast; (3) no forward model is needed, while the scalp surface can be non-spherical; (4) the method can be spatially selective to only incorporate the influence of sources in a desired region-of-interest. In addition to the theoretical developments, we aim at a fully functional EEG source imaging method that can be applied to and validated by experimental data; i.e., for focal epileptic activity and evoked response potentials (ERPs). The method’s flexibility, such as the possibility to apply local analytic sensing and to resolve multi-pole source models, makes it a great candidate to go beyond current state-of-the-art EEG source imaging.
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