Controlling the Wheelchair by Eye Movements Using EEG

Van Cam Thi Le; Nhan Thanh Le; Hai Ngoc Nguyen; Dang Cao Le; Keiji Iramina

doi:10.1007/978-981-13-5859-3_41

Controlling the Wheelchair by Eye Movements Using EEG

Van Cam Thi Le, Nhan Thanh Le, Hai Ngoc Nguyen, Dang Cao Le, Keiji Iramina

Intelligence Science

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

Abstract

In this study, we propose a method to control the wheelchair by eye movement using Electroencephalography (EEG). Firstly, we collect EEG signal by five types of eye movement: Blink, Double blink, look at Right, look at Left and Relax. These movements correspond to five directions of wheelchair motion: Go forward, Go backward, Turn right, Turn left and Stop. After that, the offline EEG signal is analyzed using MATLAB to find out the classified threshold of the signal amplitude in Alpha band and Delta band. Finally, an effective algorithm is built allowing us to identify the type of eye movement and control the external device—the powered wheelchair. As the result, the average accuracy for five motion directions (Go forward, Go backward, Turn right, Turn left and Stop) are 92.333, 93, 81.667, 86.667 and 83% respectively. With this study, we expect it can give people the help they need and be applied to many fields in the near future.

Original language	English
Title of host publication	7th International Conference on the Development of Biomedical Engineering in Vietnam (BME7) - Translational Health Science and Technology for Developing Countries, 2018
Editors	Vo Van Toi, Trung Quoc Le, Hoan Thanh Ngo, Thi-Hiep Nguyen
Publisher	Springer Verlag
Pages	231-234
Number of pages	4
ISBN (Print)	9789811358586
DOIs	https://doi.org/10.1007/978-981-13-5859-3_41
Publication status	Published - 2020
Event	7th International Conference on the Development of Biomedical Engineering in Vietnam, BME 2018 - Ho Chi Minh, Viet Nam Duration: Jun 27 2018 → Jun 29 2018

Publication series

Name	IFMBE Proceedings
Volume	69
ISSN (Print)	1680-0737
ISSN (Electronic)	1433-9277

Conference

Conference	7th International Conference on the Development of Biomedical Engineering in Vietnam, BME 2018
Country/Territory	Viet Nam
City	Ho Chi Minh
Period	6/27/18 → 6/29/18

All Science Journal Classification (ASJC) codes

Bioengineering
Biomedical Engineering

Access to Document

10.1007/978-981-13-5859-3_41

Cite this

Le, V. C. T., Le, N. T., Nguyen, H. N., Le, D. C., & Iramina, K. (2020). Controlling the Wheelchair by Eye Movements Using EEG. In V. Van Toi, T. Q. Le, H. T. Ngo, & T-H. Nguyen (Eds.), 7th International Conference on the Development of Biomedical Engineering in Vietnam (BME7) - Translational Health Science and Technology for Developing Countries, 2018 (pp. 231-234). (IFMBE Proceedings; Vol. 69). Springer Verlag. https://doi.org/10.1007/978-981-13-5859-3_41

Controlling the Wheelchair by Eye Movements Using EEG. / Le, Van Cam Thi; Le, Nhan Thanh; Nguyen, Hai Ngoc et al.
7th International Conference on the Development of Biomedical Engineering in Vietnam (BME7) - Translational Health Science and Technology for Developing Countries, 2018. ed. / Vo Van Toi; Trung Quoc Le; Hoan Thanh Ngo; Thi-Hiep Nguyen. Springer Verlag, 2020. p. 231-234 (IFMBE Proceedings; Vol. 69).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

Le, VCT, Le, NT, Nguyen, HN, Le, DC & Iramina, K 2020, Controlling the Wheelchair by Eye Movements Using EEG. in V Van Toi, TQ Le, HT Ngo & T-H Nguyen (eds), 7th International Conference on the Development of Biomedical Engineering in Vietnam (BME7) - Translational Health Science and Technology for Developing Countries, 2018. IFMBE Proceedings, vol. 69, Springer Verlag, pp. 231-234, 7th International Conference on the Development of Biomedical Engineering in Vietnam, BME 2018, Ho Chi Minh, Viet Nam, 6/27/18. https://doi.org/10.1007/978-981-13-5859-3_41

Le VCT, Le NT, Nguyen HN, Le DC, Iramina K. Controlling the Wheelchair by Eye Movements Using EEG. In Van Toi V, Le TQ, Ngo HT, Nguyen T-H, editors, 7th International Conference on the Development of Biomedical Engineering in Vietnam (BME7) - Translational Health Science and Technology for Developing Countries, 2018. Springer Verlag. 2020. p. 231-234. (IFMBE Proceedings). doi: 10.1007/978-981-13-5859-3_41

Le, Van Cam Thi ; Le, Nhan Thanh ; Nguyen, Hai Ngoc et al. / Controlling the Wheelchair by Eye Movements Using EEG. 7th International Conference on the Development of Biomedical Engineering in Vietnam (BME7) - Translational Health Science and Technology for Developing Countries, 2018. editor / Vo Van Toi ; Trung Quoc Le ; Hoan Thanh Ngo ; Thi-Hiep Nguyen. Springer Verlag, 2020. pp. 231-234 (IFMBE Proceedings).

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abstract = "In this study, we propose a method to control the wheelchair by eye movement using Electroencephalography (EEG). Firstly, we collect EEG signal by five types of eye movement: Blink, Double blink, look at Right, look at Left and Relax. These movements correspond to five directions of wheelchair motion: Go forward, Go backward, Turn right, Turn left and Stop. After that, the offline EEG signal is analyzed using MATLAB to find out the classified threshold of the signal amplitude in Alpha band and Delta band. Finally, an effective algorithm is built allowing us to identify the type of eye movement and control the external device—the powered wheelchair. As the result, the average accuracy for five motion directions (Go forward, Go backward, Turn right, Turn left and Stop) are 92.333, 93, 81.667, 86.667 and 83% respectively. With this study, we expect it can give people the help they need and be applied to many fields in the near future.",

author = "Le, {Van Cam Thi} and Le, {Nhan Thanh} and Nguyen, {Hai Ngoc} and Le, {Dang Cao} and Keiji Iramina",

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AB - In this study, we propose a method to control the wheelchair by eye movement using Electroencephalography (EEG). Firstly, we collect EEG signal by five types of eye movement: Blink, Double blink, look at Right, look at Left and Relax. These movements correspond to five directions of wheelchair motion: Go forward, Go backward, Turn right, Turn left and Stop. After that, the offline EEG signal is analyzed using MATLAB to find out the classified threshold of the signal amplitude in Alpha band and Delta band. Finally, an effective algorithm is built allowing us to identify the type of eye movement and control the external device—the powered wheelchair. As the result, the average accuracy for five motion directions (Go forward, Go backward, Turn right, Turn left and Stop) are 92.333, 93, 81.667, 86.667 and 83% respectively. With this study, we expect it can give people the help they need and be applied to many fields in the near future.

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Controlling the Wheelchair by Eye Movements Using EEG

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