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Semantic Scholar Open Access 2023 4 sitasi

The Local Subtraction Approach For EEG and MEG Forward Modeling

Malte B. Holtershinken Pia Lange Tim Erdbrugger Yvonne Buschermohle F. Wallois +38 lainnya
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EDIT: A revised version of this article has been published in the SIAM Journal on Scientific Computing, see https://epubs.siam.org/doi/full/10.1137/23M1582874. In the revised version, the name of the approach was changed from"localized subtraction"to"local subtraction". In FEM-based EEG and MEG source analysis, the subtraction approach has been proposed to simulate sensor measurements generated by neural activity. While this approach possesses a rigorous foundation and produces accurate results, its major downside is that it is computationally prohibitively expensive in practical applications. To overcome this, we developed a new approach, called the local subtraction approach. This approach is designed to preserve the mathematical foundation of the subtraction approach, while also leading to sparse right-hand sides in the FEM formulation, making it efficiently computable. We achieve this by introducing a cut-off into the subtraction, restricting its influence to the immediate neighborhood of the source. In this work, this approach will be presented, analyzed, and compared to other state-of-the-art FEM right-hand side approaches. We perform validation in multi-layer sphere models where analytical solutions exist. There, we demonstrate that the local subtraction approach is vastly more efficient than the subtraction approach. Moreover, we find that for the EEG forward problem, the local subtraction approach is less dependent on the global structure of the FEM mesh when compared to the subtraction approach. Additionally, we show the local subtraction approach to rival, and in many cases even surpass, the other investigated approaches in terms of accuracy. For the MEG forward problem, we show the local subtraction approach and the subtraction approach to produce highly accurate approximations of the volume currents close to the source.

Topik & Kata Kunci

Computer Science

Penulis (43)

M

Malte B. Holtershinken

P

Pia Lange

T

Tim Erdbrugger

Y

Yvonne Buschermohle

F

F. Wallois

A

A. Buyx

S

S. Pursiainen

J

J. Vorwerk

C

C. Engwer

C

Carsten H. Wolters Institute for Biomagnetism

B

Biosignalanalysis

U

University of Munster

M

Munster

G

Germany

I

I. Informatics

O

Otto Creutzfeldt Center for Cognitive

B

Behavioral Neuroscience

I

Inserm U1105

R

Research Group on Multimodal Analysis of Brain Function

J

Jules Verne University of Picardie

A

Amiens

F

France

P

Pediatric Department

C

Chu Picardie

i

in History

E

Ethics in Medicine

T

T. U. Munich

M

Munich

C

Computing Sciences Unit

F

Faculty of Information Technology

C

C. Sciences

T

Tampere University

T

Tampere

F

Finland.

I

I. Electrical

B

Biomedical Engineering

P

Private University for Health Sciences

M

Medical Informatics

T

Technology

H

Hall in Tyrol

M

M Austria

F

Faculty of Mathematics

C

Computer Science

Format Sitasi

APA MLA BibTeX
Holtershinken, M.B., Lange, P., Erdbrugger, T., Buschermohle, Y., Wallois, F., Buyx, A. et al. (2023). The Local Subtraction Approach For EEG and MEG Forward Modeling. https://www.semanticscholar.org/paper/9b3c5cf823a99ac9f160b72544d2a141279c2fb8

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