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Largest Lyapunov Exponent Optimization for Control of a Bionic-Hand: A Brain Computer Interface Study
This paper introduces a brain control bionic-hand, and several methods have been developed for predicting and quantifying the behavior of a non-linear system such as a brain. Non-invasive investigations on the brain were conducted by means of electroencephalograph (EEG) signal oscillations. One of t...
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Formato: | Online Artículo Texto |
Lenguaje: | English |
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Frontiers Media S.A.
2022
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Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9397699/ https://www.ncbi.nlm.nih.gov/pubmed/36188803 http://dx.doi.org/10.3389/fresc.2021.802070 |
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author | Hekmatmanesh, Amin Wu, Huapeng Handroos, Heikki |
author_facet | Hekmatmanesh, Amin Wu, Huapeng Handroos, Heikki |
author_sort | Hekmatmanesh, Amin |
collection | PubMed |
description | This paper introduces a brain control bionic-hand, and several methods have been developed for predicting and quantifying the behavior of a non-linear system such as a brain. Non-invasive investigations on the brain were conducted by means of electroencephalograph (EEG) signal oscillations. One of the prominent concepts necessary to understand EEG signals is the chaotic concept named the fractal dimension and the largest Lyapunov exponent (LLE). Specifically, the LLE algorithm called the chaotic quantifier method has been employed to compute the complexity of a system. The LLE helps us to understand how the complexity of the brain changes while making a decision to close and open a fist. The LLE has been used for a long time, but here we optimize the traditional LLE algorithm to attain higher accuracy and precision for controlling a bionic hand. In the current study, the main constant input parameters of the LLE, named the false nearest neighbor and mutual information, are parameterized and then optimized by means of the Water Drop (WD) and Chaotic Tug of War (CTW) optimizers. The optimized LLE is then employed to identify imaginary movement patterns from the EEG signals for control of a bionic hand. The experiment includes 21 subjects for recording imaginary patterns. The results illustrated that the CTW solution achieved a higher average accuracy rate of 72.31% in comparison to the traditional LLE and optimized LLE by using a WD optimizer. The study concluded that the traditional LLE required enhancement using optimization methods. In addition, the CTW approximation method has the potential for more efficient solutions in comparison to the WD method. |
format | Online Article Text |
id | pubmed-9397699 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | Frontiers Media S.A. |
record_format | MEDLINE/PubMed |
spelling | pubmed-93976992022-09-29 Largest Lyapunov Exponent Optimization for Control of a Bionic-Hand: A Brain Computer Interface Study Hekmatmanesh, Amin Wu, Huapeng Handroos, Heikki Front Rehabil Sci Rehabilitation Sciences This paper introduces a brain control bionic-hand, and several methods have been developed for predicting and quantifying the behavior of a non-linear system such as a brain. Non-invasive investigations on the brain were conducted by means of electroencephalograph (EEG) signal oscillations. One of the prominent concepts necessary to understand EEG signals is the chaotic concept named the fractal dimension and the largest Lyapunov exponent (LLE). Specifically, the LLE algorithm called the chaotic quantifier method has been employed to compute the complexity of a system. The LLE helps us to understand how the complexity of the brain changes while making a decision to close and open a fist. The LLE has been used for a long time, but here we optimize the traditional LLE algorithm to attain higher accuracy and precision for controlling a bionic hand. In the current study, the main constant input parameters of the LLE, named the false nearest neighbor and mutual information, are parameterized and then optimized by means of the Water Drop (WD) and Chaotic Tug of War (CTW) optimizers. The optimized LLE is then employed to identify imaginary movement patterns from the EEG signals for control of a bionic hand. The experiment includes 21 subjects for recording imaginary patterns. The results illustrated that the CTW solution achieved a higher average accuracy rate of 72.31% in comparison to the traditional LLE and optimized LLE by using a WD optimizer. The study concluded that the traditional LLE required enhancement using optimization methods. In addition, the CTW approximation method has the potential for more efficient solutions in comparison to the WD method. Frontiers Media S.A. 2022-02-11 /pmc/articles/PMC9397699/ /pubmed/36188803 http://dx.doi.org/10.3389/fresc.2021.802070 Text en Copyright © 2022 Hekmatmanesh, Wu and Handroos. https://creativecommons.org/licenses/by/4.0/This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms. |
spellingShingle | Rehabilitation Sciences Hekmatmanesh, Amin Wu, Huapeng Handroos, Heikki Largest Lyapunov Exponent Optimization for Control of a Bionic-Hand: A Brain Computer Interface Study |
title | Largest Lyapunov Exponent Optimization for Control of a Bionic-Hand: A Brain Computer Interface Study |
title_full | Largest Lyapunov Exponent Optimization for Control of a Bionic-Hand: A Brain Computer Interface Study |
title_fullStr | Largest Lyapunov Exponent Optimization for Control of a Bionic-Hand: A Brain Computer Interface Study |
title_full_unstemmed | Largest Lyapunov Exponent Optimization for Control of a Bionic-Hand: A Brain Computer Interface Study |
title_short | Largest Lyapunov Exponent Optimization for Control of a Bionic-Hand: A Brain Computer Interface Study |
title_sort | largest lyapunov exponent optimization for control of a bionic-hand: a brain computer interface study |
topic | Rehabilitation Sciences |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9397699/ https://www.ncbi.nlm.nih.gov/pubmed/36188803 http://dx.doi.org/10.3389/fresc.2021.802070 |
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