The inverse problem in electroencephalography using the bidomain model of electrical activity.
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| Title: | The inverse problem in electroencephalography using the bidomain model of electrical activity. |
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| Authors: | Lopez Rincon, Alejandro1 alejandro.lopezrn@hotmail.com, Shimoda, Shingo1 shimoda@brain.riken.jp |
| Source: | Journal of Neuroscience Methods. Dec2016, Vol. 274, p94-105. 12p. |
| Subjects: | Electroencephalography, Inverse problems, Neural stimulation, Nonlinear statistical models, Data analysis |
| Abstract: | Background Acquiring information about the distribution of electrical sources in the brain from electroencephalography (EEG) data remains a significant challenge. An accurate solution would provide an understanding of the inner mechanisms of the electrical activity in the brain and information about damaged tissue. New Method In this paper, we present a methodology for reconstructing brain electrical activity from EEG data by using the bidomain formulation. The bidomain model considers continuous active neural tissue coupled with a nonlinear cell model. Using this technique, we aim to find the brain sources that give rise to the scalp potential recorded by EEG measurements taking into account a non-static reconstruction. Comparison with Existing Methods We simulate electrical sources in the brain volume and compare the reconstruction to the minimum norm estimates (MNEs) and low resolution electrical tomography (LORETA) results. Then, with the EEG dataset from the EEG Motor Movement/Imagery Database of the Physiobank, we identify the reaction to visual stimuli by calculating the time between stimulus presentation and the spike in electrical activity. Finally, we compare the activation in the brain with the registered activation using the LinkRbrain platform. Results/Conclusion Our methodology shows an improved reconstruction of the electrical activity and source localization in comparison with MNE and LORETA. For the Motor Movement/Imagery Database, the reconstruction is consistent with the expected position and time delay generated by the stimuli. Thus, this methodology is a suitable option for continuously reconstructing brain potentials. [ABSTRACT FROM AUTHOR] |
| Copyright of Journal of Neuroscience Methods is the property of Elsevier B.V. and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.) | |
| Database: | Engineering Source |
| FullText | Text: Availability: 0 |
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| Header | DbId: egs DbLabel: Engineering Source An: 119559913 AccessLevel: 6 PubType: Academic Journal PubTypeId: academicJournal PreciseRelevancyScore: 0 |
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| Items | – Name: Title Label: Title Group: Ti Data: The inverse problem in electroencephalography using the bidomain model of electrical activity. – Name: Author Label: Authors Group: Au Data: <searchLink fieldCode="AR" term="%22Lopez+Rincon%2C+Alejandro%22">Lopez Rincon, Alejandro</searchLink><relatesTo>1</relatesTo><i> alejandro.lopezrn@hotmail.com</i><br /><searchLink fieldCode="AR" term="%22Shimoda%2C+Shingo%22">Shimoda, Shingo</searchLink><relatesTo>1</relatesTo><i> shimoda@brain.riken.jp</i> – Name: TitleSource Label: Source Group: Src Data: <searchLink fieldCode="JN" term="%22Journal+of+Neuroscience+Methods%22">Journal of Neuroscience Methods</searchLink>. Dec2016, Vol. 274, p94-105. 12p. – Name: Subject Label: Subjects Group: Su Data: <searchLink fieldCode="DE" term="%22Electroencephalography%22">Electroencephalography</searchLink><br /><searchLink fieldCode="DE" term="%22Inverse+problems%22">Inverse problems</searchLink><br /><searchLink fieldCode="DE" term="%22Neural+stimulation%22">Neural stimulation</searchLink><br /><searchLink fieldCode="DE" term="%22Nonlinear+statistical+models%22">Nonlinear statistical models</searchLink><br /><searchLink fieldCode="DE" term="%22Data+analysis%22">Data analysis</searchLink> – Name: Abstract Label: Abstract Group: Ab Data: Background Acquiring information about the distribution of electrical sources in the brain from electroencephalography (EEG) data remains a significant challenge. An accurate solution would provide an understanding of the inner mechanisms of the electrical activity in the brain and information about damaged tissue. New Method In this paper, we present a methodology for reconstructing brain electrical activity from EEG data by using the bidomain formulation. The bidomain model considers continuous active neural tissue coupled with a nonlinear cell model. Using this technique, we aim to find the brain sources that give rise to the scalp potential recorded by EEG measurements taking into account a non-static reconstruction. Comparison with Existing Methods We simulate electrical sources in the brain volume and compare the reconstruction to the minimum norm estimates (MNEs) and low resolution electrical tomography (LORETA) results. Then, with the EEG dataset from the EEG Motor Movement/Imagery Database of the Physiobank, we identify the reaction to visual stimuli by calculating the time between stimulus presentation and the spike in electrical activity. Finally, we compare the activation in the brain with the registered activation using the LinkRbrain platform. Results/Conclusion Our methodology shows an improved reconstruction of the electrical activity and source localization in comparison with MNE and LORETA. For the Motor Movement/Imagery Database, the reconstruction is consistent with the expected position and time delay generated by the stimuli. Thus, this methodology is a suitable option for continuously reconstructing brain potentials. [ABSTRACT FROM AUTHOR] – Name: AbstractSuppliedCopyright Label: Group: Ab Data: <i>Copyright of Journal of Neuroscience Methods is the property of Elsevier B.V. and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.) |
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| RecordInfo | BibRecord: BibEntity: Identifiers: – Type: doi Value: 10.1016/j.jneumeth.2016.09.011 Languages: – Code: eng Text: English PhysicalDescription: Pagination: PageCount: 12 StartPage: 94 Subjects: – SubjectFull: Electroencephalography Type: general – SubjectFull: Inverse problems Type: general – SubjectFull: Neural stimulation Type: general – SubjectFull: Nonlinear statistical models Type: general – SubjectFull: Data analysis Type: general Titles: – TitleFull: The inverse problem in electroencephalography using the bidomain model of electrical activity. Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: Lopez Rincon, Alejandro – PersonEntity: Name: NameFull: Shimoda, Shingo IsPartOfRelationships: – BibEntity: Dates: – D: 01 M: 12 Text: Dec2016 Type: published Y: 2016 Identifiers: – Type: issn-print Value: 01650270 Numbering: – Type: volume Value: 274 Titles: – TitleFull: Journal of Neuroscience Methods Type: main |
| ResultId | 1 |
