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Fast Approximation of EEG Forward Problem and Application to Tissue Conductivity Estimation

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Abstract

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.
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Dates and versions

hal-01563293 , version 1 (17-07-2017)

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  • HAL Id : hal-01563293 , version 1

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Kostiantyn Maksymenko, Théodore Papadopoulo, Maureen Clerc. Fast Approximation of EEG Forward Problem and Application to Tissue Conductivity Estimation. 2017. ⟨hal-01563293⟩
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