Cover Image

The classification of electrooculography signals: A significant feature identification via mutual information

Stroke is currently known as the third most frequent reason for disability worldwide where the quality of life of its survivors in terms of their daily functioning is seriously affected. Brain-Computer Interface (BCI) is a system that can acquire and transform brain activity into readable outputs. T...

Full description

Saved in:
Bibliographic Details
Main Authors: Hwa, Phua Jia, Mahendra Kumar, Jothi Letchumy, Rashid, Mamunur, Musa, Rabiu Muazu, Mohd Azraai, Mohd Razman, Norizam, Sulaiman, Rozita, Jailani, Anwar, P. P. Abdul Majeed
Format: Conference or Workshop Item
Language:English
Published: Springer 2022
Subjects:
TJ Mechanical engineering and machinery
TK Electrical engineering. Electronics Nuclear engineering
Online Access:http://umpir.ump.edu.my/id/eprint/33359/1/The%20classification%20of%20electrooculography%20signals-%20A%20significant.pdf
http://umpir.ump.edu.my/id/eprint/33359/
https://doi.org/10.1007/978-981-33-4597-3_92
Tags: Add Tag
No Tags, Be the first to tag this record!
id my.ump.umpir.33359
record_format eprints
spelling my.ump.umpir.333592023-12-19T02:03:44Z http://umpir.ump.edu.my/id/eprint/33359/ The classification of electrooculography signals: A significant feature identification via mutual information Hwa, Phua Jia Mahendra Kumar, Jothi Letchumy Rashid, Mamunur Musa, Rabiu Muazu Mohd Azraai, Mohd Razman Norizam, Sulaiman Rozita, Jailani Anwar, P. P. Abdul Majeed TJ Mechanical engineering and machinery TK Electrical engineering. Electronics Nuclear engineering Stroke is currently known as the third most frequent reason for disability worldwide where the quality of life of its survivors in terms of their daily functioning is seriously affected. Brain-Computer Interface (BCI) is a system that can acquire and transform brain activity into readable outputs. This system is particularly beneficial to the people who encounter physical challenges in carrying out their daily life as the BCI outputs can be applied to BCI-based assistive devices. One of the BCI inputs that are frequently used is the Electrooculography (EOG) signal. EOG signal is the electrical voltage emitted from the movement of our eyeballs. This study aims to extract and identify significant statistical-based time-domain features based on the EOG signals acquired that would facilitate the classification of EOG movements via Support Vector Machine (SVM). The EOG signals were obtained via BioRadio. Five healthy subjects that ranged between 22 and 30 years old were involved in the EOG data acquisition. A total of 7 statistical time-domain features, namely, mean, standard deviation, variance, median, minimum, maximum, and standard error mean were extracted from all four BioRadio channels. The Mutual Information (MI) feature selection technique was employed to identify significant features. The 70:30 hold-out cross-validation technique was used in the study. It was demonstrated from the present investigation that an excellent comparable classification on both train and test dataset is attainable even by utilising the identified features. The findings further suggest the possible application of neurorehabilitation owing to the reduced computational expense resulting from the reduced feature set. Springer 2022 Conference or Workshop Item PeerReviewed pdf en http://umpir.ump.edu.my/id/eprint/33359/1/The%20classification%20of%20electrooculography%20signals-%20A%20significant.pdf Hwa, Phua Jia and Mahendra Kumar, Jothi Letchumy and Rashid, Mamunur and Musa, Rabiu Muazu and Mohd Azraai, Mohd Razman and Norizam, Sulaiman and Rozita, Jailani and Anwar, P. P. Abdul Majeed (2022) The classification of electrooculography signals: A significant feature identification via mutual information. In: Recent Trends in Mechatronics Towards Industry 4.0. Lecture Notes in Electrical Engineering; Innovative Manufacturing, Mechatronics and Materials Forum, iM3F 2020 , 6 August 2020 , Gambang, Kuantan. 1005 -1012., 730. ISSN 1876-1100 ISBN 978-981-33-4596-6 https://doi.org/10.1007/978-981-33-4597-3_92
institution Universiti Malaysia Pahang Al-Sultan Abdullah
building UMPSA Library
collection Institutional Repository
continent Asia
country Malaysia
content_provider Universiti Malaysia Pahang Al-Sultan Abdullah
content_source UMPSA Institutional Repository
url_provider http://umpir.ump.edu.my/
language English
topic TJ Mechanical engineering and machinery
TK Electrical engineering. Electronics Nuclear engineering
spellingShingle TJ Mechanical engineering and machinery
TK Electrical engineering. Electronics Nuclear engineering
Hwa, Phua Jia
Mahendra Kumar, Jothi Letchumy
Rashid, Mamunur
Musa, Rabiu Muazu
Mohd Azraai, Mohd Razman
Norizam, Sulaiman
Rozita, Jailani
Anwar, P. P. Abdul Majeed
The classification of electrooculography signals: A significant feature identification via mutual information
description Stroke is currently known as the third most frequent reason for disability worldwide where the quality of life of its survivors in terms of their daily functioning is seriously affected. Brain-Computer Interface (BCI) is a system that can acquire and transform brain activity into readable outputs. This system is particularly beneficial to the people who encounter physical challenges in carrying out their daily life as the BCI outputs can be applied to BCI-based assistive devices. One of the BCI inputs that are frequently used is the Electrooculography (EOG) signal. EOG signal is the electrical voltage emitted from the movement of our eyeballs. This study aims to extract and identify significant statistical-based time-domain features based on the EOG signals acquired that would facilitate the classification of EOG movements via Support Vector Machine (SVM). The EOG signals were obtained via BioRadio. Five healthy subjects that ranged between 22 and 30 years old were involved in the EOG data acquisition. A total of 7 statistical time-domain features, namely, mean, standard deviation, variance, median, minimum, maximum, and standard error mean were extracted from all four BioRadio channels. The Mutual Information (MI) feature selection technique was employed to identify significant features. The 70:30 hold-out cross-validation technique was used in the study. It was demonstrated from the present investigation that an excellent comparable classification on both train and test dataset is attainable even by utilising the identified features. The findings further suggest the possible application of neurorehabilitation owing to the reduced computational expense resulting from the reduced feature set.
format Conference or Workshop Item
author Hwa, Phua Jia
Mahendra Kumar, Jothi Letchumy
Rashid, Mamunur
Musa, Rabiu Muazu
Mohd Azraai, Mohd Razman
Norizam, Sulaiman
Rozita, Jailani
Anwar, P. P. Abdul Majeed
author_facet Hwa, Phua Jia
Mahendra Kumar, Jothi Letchumy
Rashid, Mamunur
Musa, Rabiu Muazu
Mohd Azraai, Mohd Razman
Norizam, Sulaiman
Rozita, Jailani
Anwar, P. P. Abdul Majeed
author_sort Hwa, Phua Jia
title The classification of electrooculography signals: A significant feature identification via mutual information
title_short The classification of electrooculography signals: A significant feature identification via mutual information
title_full The classification of electrooculography signals: A significant feature identification via mutual information
title_fullStr The classification of electrooculography signals: A significant feature identification via mutual information
title_full_unstemmed The classification of electrooculography signals: A significant feature identification via mutual information
title_sort classification of electrooculography signals: a significant feature identification via mutual information
publisher Springer
publishDate 2022
url http://umpir.ump.edu.my/id/eprint/33359/1/The%20classification%20of%20electrooculography%20signals-%20A%20significant.pdf
http://umpir.ump.edu.my/id/eprint/33359/
https://doi.org/10.1007/978-981-33-4597-3_92
_version_ 1822923953685921792
score 13.235318

Similar Items