Performance optimized of the novel dry EEG electrodes by using the Non-Dominated Sorting Genetic Algorithms (NSGA-II)

Ming Feng Han; Lun De Liao; Yu Hang Liu; Wan Ru Wang; Bor-Shyh Lin; Chin-Teng Lin

doi:10.1109/TENCON.2010.5686152

Performance optimized of the novel dry EEG electrodes by using the Non-Dominated Sorting Genetic Algorithms (NSGA-II)

Ming Feng Han^*, Lun De Liao, Yu Hang Liu, Wan Ru Wang, Bor-Shyh Lin, Chin-Teng Lin

^*Corresponding author for this work

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

2 Scopus citations

Abstract

In this study, a optimization process was performed for the developed dry electroencephalography (EEG) electrodes by using the Non-dominated Sorting Genetic Algorithm II (NSGA-II) to minima the skin-electrode impedance. The developed dry EEG electrodes can measure the EEG signals without any gels applied and no skin preparation. However, how to find a proper skin-electrode contact area is an important issue. The contact area is directly related to the electrodes impedance and fabrication cost. Therefore, the NSGA-II is used to searching the suitable contact area and other design parameters. NSGA-II is a wieldy used optimization method, especially for the multi-objectives issues like this case. Finally, we compare the results of the simulation and experiments for ensuring the optimal process. The experiment results show that using the optimal values provided from NSGA-II can achieve the minima skin-electrode impedance. It confirms the dry electrode can be effectively used for the cognitive or other applications in the future.

Original language	English
Title of host publication	TENCON 2010 - 2010 IEEE Region 10 Conference
Pages	1710-1715
Number of pages	6
DOIs	https://doi.org/10.1109/TENCON.2010.5686152
State	Published - 1 Dec 2010
Event	2010 IEEE Region 10 Conference, TENCON 2010 - Fukuoka, Japan Duration: 21 Nov 2010 → 24 Nov 2010

Publication series

Name	IEEE Region 10 Annual International Conference, Proceedings/TENCON

Conference

Conference	2010 IEEE Region 10 Conference, TENCON 2010
Country/Territory	Japan
City	Fukuoka
Period	21/11/10 → 24/11/10

Keywords

Brain computer interface
Dry electrode
EEG
Optimal process

Access to Document

10.1109/TENCON.2010.5686152

Cite this

Han, M. F., Liao, L. D., Liu, Y. H., Wang, W. R., Lin, B-S., & Lin, C-T. (2010). Performance optimized of the novel dry EEG electrodes by using the Non-Dominated Sorting Genetic Algorithms (NSGA-II). In TENCON 2010 - 2010 IEEE Region 10 Conference (pp. 1710-1715). Article 5686152 (IEEE Region 10 Annual International Conference, Proceedings/TENCON). https://doi.org/10.1109/TENCON.2010.5686152

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title = "Performance optimized of the novel dry EEG electrodes by using the Non-Dominated Sorting Genetic Algorithms (NSGA-II)",

abstract = "In this study, a optimization process was performed for the developed dry electroencephalography (EEG) electrodes by using the Non-dominated Sorting Genetic Algorithm II (NSGA-II) to minima the skin-electrode impedance. The developed dry EEG electrodes can measure the EEG signals without any gels applied and no skin preparation. However, how to find a proper skin-electrode contact area is an important issue. The contact area is directly related to the electrodes impedance and fabrication cost. Therefore, the NSGA-II is used to searching the suitable contact area and other design parameters. NSGA-II is a wieldy used optimization method, especially for the multi-objectives issues like this case. Finally, we compare the results of the simulation and experiments for ensuring the optimal process. The experiment results show that using the optimal values provided from NSGA-II can achieve the minima skin-electrode impedance. It confirms the dry electrode can be effectively used for the cognitive or other applications in the future.",

keywords = "Brain computer interface, Dry electrode, EEG, Optimal process",

author = "Han, {Ming Feng} and Liao, {Lun De} and Liu, {Yu Hang} and Wang, {Wan Ru} and Bor-Shyh Lin and Chin-Teng Lin",

year = "2010",

month = dec,

day = "1",

doi = "10.1109/TENCON.2010.5686152",

language = "English",

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series = "IEEE Region 10 Annual International Conference, Proceedings/TENCON",

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Han, MF, Liao, LD, Liu, YH, Wang, WR, Lin, B-S & Lin, C-T 2010, Performance optimized of the novel dry EEG electrodes by using the Non-Dominated Sorting Genetic Algorithms (NSGA-II). in TENCON 2010 - 2010 IEEE Region 10 Conference., 5686152, IEEE Region 10 Annual International Conference, Proceedings/TENCON, pp. 1710-1715, 2010 IEEE Region 10 Conference, TENCON 2010, Fukuoka, Japan, 21/11/10. https://doi.org/10.1109/TENCON.2010.5686152

Performance optimized of the novel dry EEG electrodes by using the Non-Dominated Sorting Genetic Algorithms (NSGA-II). / Han, Ming Feng; Liao, Lun De; Liu, Yu Hang et al.
TENCON 2010 - 2010 IEEE Region 10 Conference. 2010. p. 1710-1715 5686152 (IEEE Region 10 Annual International Conference, Proceedings/TENCON).

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

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AU - Han, Ming Feng

AU - Liao, Lun De

AU - Liu, Yu Hang

AU - Wang, Wan Ru

AU - Lin, Bor-Shyh

AU - Lin, Chin-Teng

PY - 2010/12/1

Y1 - 2010/12/1

N2 - In this study, a optimization process was performed for the developed dry electroencephalography (EEG) electrodes by using the Non-dominated Sorting Genetic Algorithm II (NSGA-II) to minima the skin-electrode impedance. The developed dry EEG electrodes can measure the EEG signals without any gels applied and no skin preparation. However, how to find a proper skin-electrode contact area is an important issue. The contact area is directly related to the electrodes impedance and fabrication cost. Therefore, the NSGA-II is used to searching the suitable contact area and other design parameters. NSGA-II is a wieldy used optimization method, especially for the multi-objectives issues like this case. Finally, we compare the results of the simulation and experiments for ensuring the optimal process. The experiment results show that using the optimal values provided from NSGA-II can achieve the minima skin-electrode impedance. It confirms the dry electrode can be effectively used for the cognitive or other applications in the future.

AB - In this study, a optimization process was performed for the developed dry electroencephalography (EEG) electrodes by using the Non-dominated Sorting Genetic Algorithm II (NSGA-II) to minima the skin-electrode impedance. The developed dry EEG electrodes can measure the EEG signals without any gels applied and no skin preparation. However, how to find a proper skin-electrode contact area is an important issue. The contact area is directly related to the electrodes impedance and fabrication cost. Therefore, the NSGA-II is used to searching the suitable contact area and other design parameters. NSGA-II is a wieldy used optimization method, especially for the multi-objectives issues like this case. Finally, we compare the results of the simulation and experiments for ensuring the optimal process. The experiment results show that using the optimal values provided from NSGA-II can achieve the minima skin-electrode impedance. It confirms the dry electrode can be effectively used for the cognitive or other applications in the future.

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ER -

Performance optimized of the novel dry EEG electrodes by using the Non-Dominated Sorting Genetic Algorithms (NSGA-II)

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Keywords

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