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 proceedingConference contributionpeer-review

2 Scopus citations


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 languageEnglish
Title of host publicationTENCON 2010 - 2010 IEEE Region 10 Conference
Number of pages6
StatePublished - 1 Dec 2010
Event2010 IEEE Region 10 Conference, TENCON 2010 - Fukuoka, Japan
Duration: 21 Nov 201024 Nov 2010

Publication series

NameIEEE Region 10 Annual International Conference, Proceedings/TENCON


Conference2010 IEEE Region 10 Conference, TENCON 2010


  • Brain computer interface
  • Dry electrode
  • EEG
  • Optimal process


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