Volume 15, Issue 2 (March & April 2024)                   BCN 2024, 15(2): 247-260 | Back to browse issues page


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Samadi S, Zakeri B, Khanbabaie R. A Full-wave Solution of Deep Sources in the Lossy Human Head to Accurate Electroencephalography and Magnetoencephalography. BCN 2024; 15 (2) :247-260
URL: http://bcn.iums.ac.ir/article-1-2328-en.html
1- Department of Electrical Engineering, Faculty of Electrical and Computer Engineering, Babol Noshirvani University of Technology, Babol, Iran.
2- Department of Physics, University of British Columbia, Vancouver, Canada.
Abstract:  
Introduction: Currents in the brain flow inside neurons and across their boundaries into the extracellular medium, create electric and magnetic fields. These fields, which contain suitable information on brain activity, can be measured by electroencephalography (EEG), magnetoencephalography (MEG), and direct neural imaging. 
Methods: In this paper, we employed an electromagnetic model of the neuron activity and human head to derive electric and magnetic fields (brain waves) using a full-wave approach (ie. without any approximation). Currently, the brain waves are only derived using the quasi-static approximation (QSA) of Maxwell’s equations in electromagnetic theory. 
Results: As a result, source localization in brain imaging will produce some errors. So far, the error rate of the QSA on the output results of electric and magnetic fields has not been investigated. This issue has become more noticeable due to the increased sensitivity of modern electroencephalography (EEG) and magnetoencephalography (MEG) devices. This work introduces issues that QSA encounters in this problem and reveals the necessity of a full-wave solution. Then, a full-wave solution of the problem in closed-form format is presented for the first time. This solution is done in two scenarios: the source (active neurons) is in the center of a sphere, and when the source is out of the center but deeply inside the sphere. The first scenario is simpler, but the second one is much more complicated and is solved using a partial-wave series expression. 
Conclusion: One of the significant achievements of this model is improving the interpretation of EEG and MEG measurements, resulting in more accurate source localization.
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Type of Study: Original | Subject: Computational Neuroscience
Received: 2021/10/28 | Accepted: 2022/04/12 | Published: 2024/03/19

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