Mechanical Evaluation for Dry Electrode Tip Geometry in Scalp Electroencephalography Measurements

Area of Science:

• Biomedical Engineering
• Neuroscience
• Materials Science

Background:

• Dry electrodes in electroencephalography (EEG) face challenges balancing signal stability and user comfort, hindering long-term wearable use.
• Existing evaluations of electrode tip geometry lack quantitative, mechanics-based assessments, relying heavily on subjective feedback.

Purpose of the Study:

• To develop a quantitative framework for optimizing dry EEG electrode tip geometry.
• To identify an electrode tip geometry that minimizes mechanical stress on the scalp, especially under tilted contact conditions.

Main Methods:

• Utilized finite element analysis (FEA) to assess mechanical stress using strain energy density (SED).
• Analyzed six electrode tip geometries (varying fillet radius to prong radius ratio) under 0-5 degree inclination angles.
• Employed an iterative search algorithm for geometry optimization to minimize peak SED.

Main Results:

• Intermediate fillet geometries with rounded edges significantly reduced peak SED under inclined conditions.
• An optimized geometry ratio (Rrate* = 0.61875) was identified as most effective in minimizing mechanical loading.
• Quantitative analysis revealed a clear correlation between tip geometry and mechanical stress distribution.

Conclusions:

• The study provides a mechanics-based design guideline for dry EEG electrodes.
• Optimized electrode geometry can enhance user comfort by reducing scalp mechanical stress during wearable applications.
• This research addresses a critical barrier in developing practical, long-term wearable EEG systems.