Unpacking Galvanic Vestibular Stimulation using simulations and relating current flow to reported motions: Comparison...

Galvanic Vestibular Stimulation (GVS) is a non-invasive electrical stimulation technique that is typically used to probe the vestibular system. When using direct current or very low frequency sine, GVS causes postural sway or perception of illusory (virtual) motions. GVS is commonly delivered using two electrodes placed at the mastoids, however, placements involving additional electrodes / locations have been employed. Our objective was to systematically evaluate all known GVS electrode placements, compare induced current flow, and how it relates to the archetypal sway and virtual motions. The ultimate goal is to help users in having a better understanding of the effects of different placements.

We simulated seven GVS electrode placements with same total injected current using an ultra-high resolution model. Induced electric field (EF) patterns at the cortical and the level of vestibular organs (left and right) were determined. A range of current flow metrics including potential factors such as inter-electrode separation, percentage of current entering the cranial cavity, and symmetricity were calculated. Finally, we relate current flow to reported GVS motions.

As expected, current flow patterns are electrode placement specific. Placements with two electrodes generally result in higher EF magnitude. Placements with four electrodes result in lower percentage of current entering the cranial cavity. Symmetric placements do not result in similar EF values in the left and the right organs respectively- highlighting inherent anatomical asymmetry of the human head. Asymmetric placements were found to induce as much as ~3-fold higher EF in one organ over the other. The percentage of current entering the cranial cavity varies between ~15% and ~40% depending on the placement.

We expect our study to advance understanding of GVS and provide insight on probable mechanism of action of a certain electrode placement choice. The dataset generated across several metrics will support hypothesis testing relating empirical outcomes to current flow patterns. Further, the differences in current flow will guide stimulation strategy (what placement and how much scalp current to use) and facilitate a quantitatively informed rational / optimal decision.