3-D Printed Watermill-Like Semi-Dry Electrodes for BCI Applications.

Area of Science:

• Neuroscience and Biomedical Engineering
• Focus on developing practical solutions for electroencephalography (EEG) signal acquisition.

Background:

• Traditional wet EEG electrodes require extensive preparation and cleaning, hindering real-world Brain-Computer Interface (BCI) use.
• Semi-dry electrodes offer an alternative but face challenges with hair penetration and controlled conductive material application.
• Previous work introduced 3D-printed, watermill-shaped electrodes to address these limitations.

Purpose of the Study:

• To prototype and refine three designs of watermill-shaped EEG electrodes.
• To evaluate their performance in hair-layer penetration and conductive gel application efficiency.
• To assess their viability for real-world BCI applications across diverse hairstyles.

Main Methods:

• Development and prototyping of three distinct watermill-shaped EEG electrode designs.
• Offline experiments using eight wig styles (human and synthetic hair) to assess hair penetration and gel application.
• Real-world neurophysiological experiments with 15 participants featuring varied hairstyles.

Main Results:

• Watermill-shaped electrodes demonstrated significantly reduced conductive gel consumption compared to traditional wet electrodes (p<0.001).
• The 'star' electrode design required the least amount of gel (mean 1.94 rolls) to achieve target impedance.
• Effective performance across different hairstyles, ensuring consistent hair penetration and controlled gel application.

Conclusions:

• The 3D-printed watermill-shaped electrodes represent a viable semi-dry solution for EEG recording.
• These electrodes overcome practical challenges associated with hair and gel management in BCI systems.
• The findings support their adoption for continuous EEG monitoring in real-world BCI scenarios.