The classification of Electrooculogram (EOG) through the application of Linear Discriminant Analysis (LDA) of selected time-domain signals
Recently, Human Computer Interface (HCI) has been studied extensively to handle electromechanical rehabilitation aids using different bio-signals. Among various bio-signals, electrooculogram (EOG) signal have been studied in depth due to its significant signal pattern stability. The primary goal of...
| Main Authors: | , , , , , , , |
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| Format: | Conference or Workshop Item |
| Language: | English English |
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Springer Science and Business Media Deutschland GmbH
2022
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| Online Access: | https://umpir.ump.edu.my/id/eprint/42237/ |
| _version_ | 1848827306849599488 |
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| author | Farhan Anis, Azhar Mahfuzah, Mustafa Norizam, Sulaiman Mamunur, Rashid Bari, Bifta Sama Islam, Md Nahidul Hasan, Md Jahid Nur Fahriza, Mohd Ali |
| author_facet | Farhan Anis, Azhar Mahfuzah, Mustafa Norizam, Sulaiman Mamunur, Rashid Bari, Bifta Sama Islam, Md Nahidul Hasan, Md Jahid Nur Fahriza, Mohd Ali |
| author_sort | Farhan Anis, Azhar |
| building | UMP Institutional Repository |
| collection | Online Access |
| description | Recently, Human Computer Interface (HCI) has been studied extensively to handle electromechanical rehabilitation aids using different bio-signals. Among various bio-signals, electrooculogram (EOG) signal have been studied in depth due to its significant signal pattern stability. The primary goal of EOG based HCI is to control assistive devices using eye movement which can be utilized to rehabilitate the disabled people. In this paper, a novel approach of four classes EOG has been proposed to investigate the possibility of real-life HCI application. A variety of time-domain based EOG features including mean, root mean square (RMS), maximum, variance, minimum, medium, skewness and standard deviation have been explored. The extracted features have been classified by the linear discriminant analysis (LDA) with the classification accuracy of training accuracy (90.43%) and testing accuracy (88.89%). The obtained accuracy is very encouraging to be utilized in HCI technology in the purpose of assisting physically disabled patients. Total 10 participants have been contributed to record EOG data and the range between 21 and 29 years old. |
| first_indexed | 2025-11-15T03:46:43Z |
| format | Conference or Workshop Item |
| id | ump-42237 |
| institution | Universiti Malaysia Pahang |
| institution_category | Local University |
| language | English English |
| last_indexed | 2025-11-15T03:58:37Z |
| publishDate | 2022 |
| publisher | Springer Science and Business Media Deutschland GmbH |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | ump-422372025-09-03T00:30:54Z https://umpir.ump.edu.my/id/eprint/42237/ The classification of Electrooculogram (EOG) through the application of Linear Discriminant Analysis (LDA) of selected time-domain signals Farhan Anis, Azhar Mahfuzah, Mustafa Norizam, Sulaiman Mamunur, Rashid Bari, Bifta Sama Islam, Md Nahidul Hasan, Md Jahid Nur Fahriza, Mohd Ali T Technology (General) TA Engineering (General). Civil engineering (General) TJ Mechanical engineering and machinery TK Electrical engineering. Electronics Nuclear engineering TS Manufactures Recently, Human Computer Interface (HCI) has been studied extensively to handle electromechanical rehabilitation aids using different bio-signals. Among various bio-signals, electrooculogram (EOG) signal have been studied in depth due to its significant signal pattern stability. The primary goal of EOG based HCI is to control assistive devices using eye movement which can be utilized to rehabilitate the disabled people. In this paper, a novel approach of four classes EOG has been proposed to investigate the possibility of real-life HCI application. A variety of time-domain based EOG features including mean, root mean square (RMS), maximum, variance, minimum, medium, skewness and standard deviation have been explored. The extracted features have been classified by the linear discriminant analysis (LDA) with the classification accuracy of training accuracy (90.43%) and testing accuracy (88.89%). The obtained accuracy is very encouraging to be utilized in HCI technology in the purpose of assisting physically disabled patients. Total 10 participants have been contributed to record EOG data and the range between 21 and 29 years old. Springer Science and Business Media Deutschland GmbH 2022 Conference or Workshop Item PeerReviewed pdf en https://umpir.ump.edu.my/id/eprint/42237/1/The%20classification%20of%20Electrooculogram%20%28EOG%29.pdf pdf en https://umpir.ump.edu.my/id/eprint/42237/2/The%20classification%20of%20Electrooculogram%20%28EOG%29%20through%20the%20application%20of%20Linear%20Discriminant%20Analysis%20%28LDA%29%20of%20selected%20time-domain%20signals_ABS.pdf Farhan Anis, Azhar and Mahfuzah, Mustafa and Norizam, Sulaiman and Mamunur, Rashid and Bari, Bifta Sama and Islam, Md Nahidul and Hasan, Md Jahid and Nur Fahriza, Mohd Ali (2022) The classification of Electrooculogram (EOG) through the application of Linear Discriminant Analysis (LDA) of selected time-domain signals. In: Lecture Notes in Electrical Engineering. Innovative Manufacturing, Mechatronics and Materials Forum, iM3F 2020 , 6 August 2020 , Gambang. pp. 583-591., 730. ISSN 1876-1100 ISBN 978-981334596-6 (Published) https://doi.org/10.1007/978-981-33-4597-3_53 |
| spellingShingle | T Technology (General) TA Engineering (General). Civil engineering (General) TJ Mechanical engineering and machinery TK Electrical engineering. Electronics Nuclear engineering TS Manufactures Farhan Anis, Azhar Mahfuzah, Mustafa Norizam, Sulaiman Mamunur, Rashid Bari, Bifta Sama Islam, Md Nahidul Hasan, Md Jahid Nur Fahriza, Mohd Ali The classification of Electrooculogram (EOG) through the application of Linear Discriminant Analysis (LDA) of selected time-domain signals |
| title | The classification of Electrooculogram (EOG) through the application of Linear Discriminant Analysis (LDA) of selected time-domain signals |
| title_full | The classification of Electrooculogram (EOG) through the application of Linear Discriminant Analysis (LDA) of selected time-domain signals |
| title_fullStr | The classification of Electrooculogram (EOG) through the application of Linear Discriminant Analysis (LDA) of selected time-domain signals |
| title_full_unstemmed | The classification of Electrooculogram (EOG) through the application of Linear Discriminant Analysis (LDA) of selected time-domain signals |
| title_short | The classification of Electrooculogram (EOG) through the application of Linear Discriminant Analysis (LDA) of selected time-domain signals |
| title_sort | classification of electrooculogram (eog) through the application of linear discriminant analysis (lda) of selected time-domain signals |
| topic | T Technology (General) TA Engineering (General). Civil engineering (General) TJ Mechanical engineering and machinery TK Electrical engineering. Electronics Nuclear engineering TS Manufactures |
| url | https://umpir.ump.edu.my/id/eprint/42237/ https://umpir.ump.edu.my/id/eprint/42237/ |