Structural Stability of Flat Electroencephalography
Flat Electroencephalography is a way of viewing electroencephalography signals on the first component of Fuzzy Topograhic Topological Mapping (FTTM), a model which was designed to solve neuromagnetic inverse problem. This novel method is well known for its ability to preserves the orientation and ma...
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Zhengzhou University, Marsland Press
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ump-59132015-03-03T09:27:11Z http://umpir.ump.edu.my/id/eprint/5913/ Structural Stability of Flat Electroencephalography Tan, Lit Ken Tahir, Ahmad R Medicine (General) RC0321 Neuroscience. Biological psychiatry. Neuropsychiatry QA Mathematics Flat Electroencephalography is a way of viewing electroencephalography signals on the first component of Fuzzy Topograhic Topological Mapping (FTTM), a model which was designed to solve neuromagnetic inverse problem. This novel method is well known for its ability to preserves the orientation and magnitude of EEG sensors and signals. However, this preservation renders Flat EEG to contain unwanted signals captured during recording from the surroundings. Consequently, its accuracy in depicting actual electrical activity inside the brain is affected. Present of artifacts would pose a serious problem if it is large. Thusly, this study will investigate the persistence of Flat EEG to surrounding “noises” from dynamic viewpoint by means of structural stability. Basically, it will be showed that Flat EEG in the presence of “noises” may still reflects the actual electrical activity inside the brain, if the contaminated Flat EEG falls within a class of dynamical systems. Zhengzhou University, Marsland Press 2014-04-26 Article PeerReviewed application/pdf en http://umpir.ump.edu.my/id/eprint/5913/1/Structural_Stability_of_Flat_Electroencephalography.pdf Tan, Lit Ken and Tahir, Ahmad (2014) Structural Stability of Flat Electroencephalography. Life Science Journal, 11 (8). pp. 165-170. ISSN 1097-8135 http://www.lifesciencesite.com/lsj/life1108/ |
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Online Access |
language |
English |
topic |
R Medicine (General) RC0321 Neuroscience. Biological psychiatry. Neuropsychiatry QA Mathematics |
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R Medicine (General) RC0321 Neuroscience. Biological psychiatry. Neuropsychiatry QA Mathematics Tan, Lit Ken Tahir, Ahmad Structural Stability of Flat Electroencephalography |
description |
Flat Electroencephalography is a way of viewing electroencephalography signals on the first component of Fuzzy Topograhic Topological Mapping (FTTM), a model which was designed to solve neuromagnetic inverse problem. This novel method is well known for its ability to preserves the orientation and magnitude of EEG sensors and signals. However, this preservation renders Flat EEG to contain unwanted signals captured during recording from the surroundings. Consequently, its accuracy in depicting actual electrical activity inside the brain is affected. Present of artifacts would pose a serious problem if it is large. Thusly, this study will investigate the persistence of Flat EEG to surrounding “noises” from dynamic viewpoint by means of structural stability. Basically, it will be showed that Flat EEG in the presence of “noises” may still reflects the actual electrical activity inside the brain, if the contaminated Flat EEG falls within a class of dynamical systems. |
format |
Article |
author |
Tan, Lit Ken Tahir, Ahmad |
author_facet |
Tan, Lit Ken Tahir, Ahmad |
author_sort |
Tan, Lit Ken |
title |
Structural Stability of Flat Electroencephalography |
title_short |
Structural Stability of Flat Electroencephalography |
title_full |
Structural Stability of Flat Electroencephalography |
title_fullStr |
Structural Stability of Flat Electroencephalography |
title_full_unstemmed |
Structural Stability of Flat Electroencephalography |
title_sort |
structural stability of flat electroencephalography |
publisher |
Zhengzhou University, Marsland Press |
publishDate |
2014 |
url |
http://umpir.ump.edu.my/id/eprint/5913/ http://umpir.ump.edu.my/id/eprint/5913/ http://umpir.ump.edu.my/id/eprint/5913/1/Structural_Stability_of_Flat_Electroencephalography.pdf |
first_indexed |
2023-09-18T22:01:20Z |
last_indexed |
2023-09-18T22:01:20Z |
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1777414418733203456 |