Comparing performance of dry and gel EEG electrodes in VR using MI paradigms

Ahmadi, M., Farrokhi Nia, A., Michalka, S.W., Sumich, A.L. ORCID: 0000-0003-4333-8442, Wuensche, B. and Billinghurst, M., 2023. Comparing performance of dry and gel EEG electrodes in VR using MI paradigms. In: VRST 2023: Proceedings of the 29th ACM Symposium on Virtual Reality Software and Technology. New York: ACM. ISBN 9798400703287

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Abstract

Brain–computer interfaces (BCIs) are an emerging technology with numerous applications. Electroencephalogram (EEG) motor imagery (MI) is among the most common BCI paradigms and has been used extensively in healthcare applications such as post-stroke rehabilitation. Using a Virtual Reality (VR) game, Push Me, we con-ducted a pilot study to compare MI accuracy with Gel or active-dry EEG electrodes. The motivation was to (1) investigate the MI paradigm in a VR environment and (2) compare MI accuracy using active dry and gel electrodes with different Machine Learning (ML)classifications (SVM, KNN and RF). The results indicate that while gel-based electrodes, in combination with SVM, achieved the high-est accuracy, dry electrode EEG caps achieved similar outcomes, especially with SVM and KNN models.

Item Type:	Chapter in book
Description:	Paper presented at VRST 2023: 29th ACM Symposium on Virtual Reality Software and Technology, Christchurch, New Zealand, 9-11 October 2023. Article no. 46
Creators:	Ahmadi, M., Farrokhi Nia, A., Michalka, S.W., Sumich, A.L., Wuensche, B. and Billinghurst, M.
Publisher:	ACM
Place of Publication:	New York
Date:	9 October 2023
ISBN:	9798400703287
Identifiers:	Number Type 10.1145/3611659.3617436 DOI 1820582 Other
Divisions:	Schools > School of Social Sciences
Record created by:	Jonathan Gallacher
Date Added:	23 Oct 2023 09:43
Last Modified:	23 Oct 2023 09:47
URI:	https://irep.ntu.ac.uk/id/eprint/50072

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