Comparing Performance of Dry and Gel EEG Electrodes in VR using MI Paradigms

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 conducted 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 highest accuracy, dry electrode EEG caps achieved similar outcomes, especially with SVM and KNN models.