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Institutional Repository [SANDBOX]
Technical University of Crete
Technical University of Crete
EN
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| URI: | http://purl.tuc.gr/dl/dias/2AB35C19-AAFB-4DEF-91ED-EB6396E764B0 | ||
| Year | 2022 | ||
| Type of Item | Conference Full Paper | ||
| License |
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| Bibliographic Citation | M. Samara, C. Farmaki, N. Zacharioudakis, M. Pediaditis, M. Krana and V. Sakkalis, "Comparison between dry and wet EEG electrodes in an SSVEP-based BCI for robot navigation," in Proceedings of the 2022 IEEE 22nd International Conference on Bioinformatics and Bioengineering (BIBE 2022), Taichung, Taiwan, 2022, pp. 333-338, doi: 10.1109/BIBE55377.2022.00075. https://doi.org/10.1109/BIBE55377.2022.00075 | ||
| Appears in Collections |
Electroencephalography-based brain computer interfaces (BCIs) have been widely used in assistive applications for patients suffering from quadriplegia, or even the locked-in syndrome, to promote autonomy and control. Steady-state visual evoked potentials (SSVEPs) is a BCI stimulation protocol that has been employed widely in navigation applications, due to their efficiency and fast response time. In the current study, we use a previously developed SSVEP-based BCI for robotic car navigation with a low-cost EEG recording device, to compare the performance of the easier-to-use dry EEG-electrodes and the commonly-used wet electrodes. We also employ two different stimulus scenarios, a fixed one and an adaptive one optimized for each participant. We tested our system on 23 healthy participants in both an offline session and an online navigation task on a predefined route. The results indicate that our system with the combination of the low-cost EEG device and the dry electrodes achieves comparable performance to the same system using wet electrodes. Moreover, it ensures a more user-friendly and affordable alternative that could be adopted by a larger part of potential users.
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