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

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.