Abstract In electroencephalography (EEG) measurements using dry electrodes, a trade-off between signal stability and user comfort is a critical barrier to long-term, wearable applications. While various approaches have been proposed to address this issue, the mechanical impact of electrode tip geometry has not been adequately quantified, and most existing evaluations predominantly rely on subjective assessments. To address this gap with a quantitative, mechanics-based framework, the current study aimed to identify an optimized electrode tip geometry that minimizes mechanical stress on the scalp even under tilted contact conditions. Finite element analysis was conducted using strain energy density (SED), a mechanical index known to correlate with neural impulse activity, as a quantitative indicator of the mechanical influence of tip geometry on the skin. Six types of electrode tip geometries, ranging from flat to hemispherical, were defined based on the ratio of fillet radius to prong radius. These geometries were analyzed under inclination angles from 0 to 5 degree, and their peak SED values were compared. Additionally, a geometry optimization using an iterative search algorithm was performed to minimize peak SED under the 5 degree tillt. The findings revealed that intermediate fillet geometries with gently rounded edges more effectively reduce peak SED under inclined conditions. Optimization further identified a geometry ratio of Rrate* = 0.61875 as the most effective tip geometry for minimizing mechanical loading under the specified conditions. These results offer a potential geometric design guideline for dry EEG electrodes that can help maintain user comfort across varying inclination angles.
Araki et al. (Wed,) studied this question.