Comparative Analysis of Parallel Brain Activity Mapping Algorithms for High Resolution Brain Models

Keywords: Parallelized Algorithms, optimization, brain mapping, electroencephalography

Abstract

This paper proposes a comparative analysis between regular and parallel versions of FISTA and Tikhonov-like optimizations for solving the EEG brain mapping problem. Such comparison is performed in terms of computational time reduction and estimation error achieved by the parallelized methods. Two brain models (high- and low-resolution) are used to compare the algorithms. As a result, it can be seen that, if the number of parallel processes increases, computational time decreases significantly for all the head models used in this work, without compromising the reconstruction quality. In addition, it can be concluded that the use of a high-resolution head model produces an improvement in any source reconstruction method in terms of spatial resolution.

 

Author Biographies

Cristhian D. Molina-Machado , Universidad Tecnológica de Pereira, Colombia

M.Sc. en Ingeniería Eléctrica, Facultad de Ingeniería, Universidad Tecnológica de Pereira, Pereira-Colombia, cdmolina@utp.edu.co

Ernesto Cuartas , KU Leuven University, Bélgica

PhD. en Ingeniería, Movement Control & Neuroplasticity Research Group, KU Leuven University, Leuven-Belgium, ernesto.cuartas@kuleuven.be

Juan D. Martínez-Vargas*, Instituto Tecnológico Metropolitano, Colombia

PhD. en Ingeniería, Laboratorio de Máquinas inteligentes y reconocimientos de patrones, Instituto Tecnológico Metropolitano, Medellín-Colombia, juanmartinez@itm.edu.co

Eduardo Giraldo , Universidad Tecnológica de Pereira, Colombia

PhD. en Ingeniería, Programa de Ingeniería Eléctrica, Facultad de Ingenierías, Universidad Tecnológica de Pereira, Pereira-Colombia, egiraldos@utp.edu.co

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How to Cite
Molina-Machado , C. D., Cuartas , E., Martínez-Vargas, J. D., & Giraldo , E. (2019). Comparative Analysis of Parallel Brain Activity Mapping Algorithms for High Resolution Brain Models. TecnoLógicas, 22(46), 233-243. https://doi.org/10.22430/22565337.1344

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Published
2019-09-20
Section
Research Papers