Efficient high resolution sLORETA in brain source localization

Journal of Neural Engineering
Younes Sadat-Nejad, Soosan Beheshti

Abstract

Estimation of the source location within the brain from electroencephalography (EEG) and Magnetoencephalography (MEG) measures is a challenging task. Among the existing techniques in the filed, which are known as brain imaging methods, standardized low-resolution brain electromagnetic tomography (sLORETA) is the most popular method due to its simplicity and high accuracy. However, in this work, we illustrate that sLORETA is still noisy, and the additive noise is causing the blurry image. The existing pre-fixed/manual thresholding process after sLORETA can partially take care of denoising. However, this ad-hoc thresholding can either remove so much of the desired data or leave much of the noise in the process. Manual correction to avoid such extreme cases can be time-consuming. The objective of this paper is to automate the denoising process in the form of adaptive thresholding. The proposed method, denoted by Efficient High-Resolution sLORETA (EHR-sLORETA), is based on minimizing the error between the desired denoised source and the source estimates. The approach is evaluated using synthetic EEG and real EEG data. Spatial Dispersion (SD), and Mean Square Error (MSE) are used as metrics to provide the quantitative performance of...Continue Reading

References

Aug 1, 1979·Electroencephalography and Clinical Neurophysiology·B N Cuffin, D Cohen
Jan 1, 1993·Journal of Cerebral Blood Flow and Metabolism : Official Journal of the International Society of Cerebral Blood Flow and Metabolism·K J FristonR S Frackowiak
Dec 24, 2002·Clinical Neurophysiology : Official Journal of the International Federation of Clinical Neurophysiology·G LantzC M Michel
Mar 24, 2004·Neurology·A PalminiH V Vinters
Sep 8, 2004·Clinical Neurophysiology : Official Journal of the International Federation of Clinical Neurophysiology·Christoph M MichelRolando Grave de Peralta
Mar 16, 2007·IEEE Transactions on Bio-medical Engineering·Peng XuDezhong Yao
Jul 22, 2008·Alzheimer's & Dementia : the Journal of the Alzheimer's Association·Barry ReisbergMony J de Leon
Nov 8, 2008·Journal of Neuroengineering and Rehabilitation·Roberta GrechBart Vanrumste
Sep 8, 2010·Biomedical Engineering Online·Alexandre GramfortMaureen Clerc
Mar 26, 2011·Computational Intelligence and Neuroscience·Alexandre GramfortMaureen Clerc
May 18, 2011·Computational Intelligence and Neuroscience·François TadelRichard M Leahy
May 28, 2011·Human Brain Mapping·Matthias DümpelmannAndreas Schulze-Bonhage
Apr 3, 2013·Journal of Clinical Neurophysiology : Official Publication of the American Electroencephalographic Society·Rishi V A SheorajpandayPeter P De Deyn
Sep 24, 2013·International Journal of Psychophysiology : Official Journal of the International Organization of Psychophysiology·Yingying TangJijun Wang
Oct 29, 2013·NeuroImage·Alexandre GramfortMatti S Hämäläinen
May 23, 2014·IEEE Transactions on Bio-medical Engineering·Yunfeng LuBin He
Jul 12, 2014·Progress in Neurobiology·Pieter van MierloDaniele Marinazzo
Aug 5, 2014·Clinical Neurophysiology : Official Journal of the International Federation of Clinical Neurophysiology·Abbas SohrabpourBin He
Nov 2, 2014·Australasian Physical & Engineering Sciences in Medicine·Munsif Ali JatoiIbrahima Faye
Nov 23, 2016·IEEE Transactions on Bio-medical Engineering·Chunsheng LiBerj L Bardakjian
Aug 18, 2017·Journal of Neural Engineering·Jussi T Lindgren
Jun 26, 2018·General Physiology and Biophysics·Milan Mitka, Igor Riečanský
Jul 27, 2018·IEEE Transactions on Bio-medical Engineering·Seyed Amir Hossein HosseiniBin He
Jan 4, 2019·IEEE Transactions on Bio-medical Engineering·Ke LiuYuanqing Li
Apr 26, 2019·Frontiers in Neurology·Christoph M Michel, Denis Brunet
Jan 18, 2020·Conference Proceedings : ... Annual International Conference of the IEEE Engineering in Medicine and Biology Society·Younes Sadat-Nejad, Soosan Beheshti

❮ Previous
Next ❯

Related Concepts

Related Feeds

Brain-Computer Interface

A brain-computer interface, also known as a brain-machine interface, is a bi-directional communication pathway between an external device and a wired brain. Here is the latest research on this topic.

Related Papers

Conference Proceedings : ... Annual International Conference of the IEEE Engineering in Medicine and Biology Society
Younes Sadat-Nejad, Soosan Beheshti
Nonlinear Dynamics, Psychology, and Life Sciences
Ateke GoshvarpourAtefeh Goshvarpour
Journal of Clinical Neurophysiology : Official Publication of the American Electroencephalographic Society
Octavian V LieÁkos C Szabó
© 2021 Meta ULC. All rights reserved