EOG-sEMG Human Interface for Communication
The aim of this study is to present electrooculogram (EOG) and surface electromyogram (sEMG) signals that can be used as a human-computer interface. Establishing an efficient alternative channel for communication without overt speech and hand movements is important for increasing the quality of life...
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2016-01-01
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Series: | Computational Intelligence and Neuroscience |
Online Access: | http://dx.doi.org/10.1155/2016/7354082 |
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doaj-939961fad25e46fbaab3aaf3eee62f382020-11-24T22:54:15ZengHindawi LimitedComputational Intelligence and Neuroscience1687-52651687-52732016-01-01201610.1155/2016/73540827354082EOG-sEMG Human Interface for CommunicationHiroki Tamura0Mingmin Yan1Keiko Sakurai2Koichi Tanno3Department of Environmental Robotics, University of Miyazaki, Miyazaki 889-2192, JapanOrganization for Promotion of “Center of Community” Program, University of Miyazaki, Miyazaki 889-2192, JapanInterdisciplinary Graduate School of Agriculture and Engineering, University of Miyazaki, Miyazaki 889-2192, JapanDepartment of Electrical and Systems Engineering, University of Miyazaki, Miyazaki 889-2192, JapanThe aim of this study is to present electrooculogram (EOG) and surface electromyogram (sEMG) signals that can be used as a human-computer interface. Establishing an efficient alternative channel for communication without overt speech and hand movements is important for increasing the quality of life for patients suffering from amyotrophic lateral sclerosis, muscular dystrophy, or other illnesses. In this paper, we propose an EOG-sEMG human-computer interface system for communication using both cross-channels and parallel lines channels on the face with the same electrodes. This system could record EOG and sEMG signals as “dual-modality” for pattern recognition simultaneously. Although as much as 4 patterns could be recognized, dealing with the state of the patients, we only choose two classes (left and right motion) of EOG and two classes (left blink and right blink) of sEMG which are easily to be realized for simulation and monitoring task. From the simulation results, our system achieved four-pattern classification with an accuracy of 95.1%.http://dx.doi.org/10.1155/2016/7354082 |
collection |
DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
author |
Hiroki Tamura Mingmin Yan Keiko Sakurai Koichi Tanno |
spellingShingle |
Hiroki Tamura Mingmin Yan Keiko Sakurai Koichi Tanno EOG-sEMG Human Interface for Communication Computational Intelligence and Neuroscience |
author_facet |
Hiroki Tamura Mingmin Yan Keiko Sakurai Koichi Tanno |
author_sort |
Hiroki Tamura |
title |
EOG-sEMG Human Interface for Communication |
title_short |
EOG-sEMG Human Interface for Communication |
title_full |
EOG-sEMG Human Interface for Communication |
title_fullStr |
EOG-sEMG Human Interface for Communication |
title_full_unstemmed |
EOG-sEMG Human Interface for Communication |
title_sort |
eog-semg human interface for communication |
publisher |
Hindawi Limited |
series |
Computational Intelligence and Neuroscience |
issn |
1687-5265 1687-5273 |
publishDate |
2016-01-01 |
description |
The aim of this study is to present electrooculogram (EOG) and surface electromyogram (sEMG) signals that can be used as a human-computer interface. Establishing an efficient alternative channel for communication without overt speech and hand movements is important for increasing the quality of life for patients suffering from amyotrophic lateral sclerosis, muscular dystrophy, or other illnesses. In this paper, we propose an EOG-sEMG human-computer interface system for communication using both cross-channels and parallel lines channels on the face with the same electrodes. This system could record EOG and sEMG signals as “dual-modality” for pattern recognition simultaneously. Although as much as 4 patterns could be recognized, dealing with the state of the patients, we only choose two classes (left and right motion) of EOG and two classes (left blink and right blink) of sEMG which are easily to be realized for simulation and monitoring task. From the simulation results, our system achieved four-pattern classification with an accuracy of 95.1%. |
url |
http://dx.doi.org/10.1155/2016/7354082 |
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