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Journal Articles IEEE Transactions on Medical Imaging Year : 2019

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-01890242 , version 1 (08-10-2018)
hal-01890242 , version 2 (27-08-2019)

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Kostiantyn Maksymenko, Maureen Clerc, Théodore Papadopoulo. Fast Approximation of EEG Forward Problem and Application to Tissue Conductivity Estimation. IEEE Transactions on Medical Imaging, In press, ⟨10.1109/TMI.2019.2936921⟩. ⟨hal-01890242v2⟩
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