Brain Source Localization Using Stochastic Gradient Descent

Sajedah Al-Momani

doi:10.1109/JSEN.2020.3047666

2021-03-15

PRE

DOI：10.1109/JSEN.2020.3047666

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摘要

En 中文

The high temporal resolution of the electroencephalogram (EEG) makes it a useful modality to study the dynamic nature of cortical activity. The main objective of this work is to localize and track active sources on the surface of the cortex using EEG recordings. A stochastic gradient descent method is proposed, and the performance of this method is investigated on simulated data that models spatially fixed and spatially moving sources. Simulation results show that the proposed method is able to recover closely spaced sources and yields a localization accuracy similar to that of existing methods but with significantly reduced computational time. The proposed method is also applied on clinical data from epileptic patients, and the results are found to be compatible with the clinical reports.

Keyword:

Brain modeling

Electroencephalography

Lead

Inverse problems

Stochastic processes

Position measurement

Numerical models

Electroencephalography

source localization

stochastic gradient descent

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期刊

IEEE Sensors Journal

IF:

4.5

论文数:

2.1W

被引数:

7.3W

机构

American University of Sharjah

学者数:

2.6K

论文数: 2.5K

被引数: 2.7K

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