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bioRxiv · 10.64898/2026.09.21.753100

The Dry Truth: Hair-Wetting Improves Dry Electrode EEG Signal Quality

Huang, Y.·Ferat, V.·Michela, A.·Colangelo, C.·Senziani, K.·Castonguay, L.·Vulliemoz, S.·Ros, T.
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Abstract

With the rapid advancement of clinical neuroscience and Brain-Computer Interfaces (BCIs), there is an increasing demand for convenient, user-friendly EEG recording methods suitable for diverse environments, including mobile and home-based settings. Traditional gel-based EEG systems, while reliable, are inconvenient and time-consuming, whereas dry electrode systems tend to suffer from elevated noise levels. In this study, we investigated a novel methodological manipulation aimed at enhancing dry electrode signal quality: wetting the hair directly with tap water to improve scalp electrode conductivity. To this end, we recruited 22 healthy participants and compared their resting-state (RS) EEG activity across three experimental conditions (dry, semi-dry, and gel) within a single-session design. Specifically, we analyzed electrode impedance, spectral power (SP), and functional connectivity (FC). Our results show that basic hair-wetting substantially improved mean electrode impedance by 75.7% relative to the dry condition, reduced the proportion of bad channels from approximately 40% to 15%, and increased spectral-power similarity with gel recordings by 7.7% during eyes-closed recordings, and improved FC similarity by approximately 56-70% across frequency bands. Although this method does not fully match the signal quality of traditional gel-based systems, it represents a promising compromise, enhancing EEG data quality under suboptimal conditions. This approach offers a practical and non-invasive means to improve EEG signal quality, ultimately expanding real-world applications of EEG.

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Huang, Y., Ferat, V., Michela, A., Colangelo, C., Senziani, K., Castonguay, L., Vulliemoz, S., Ros, T.. 2026-09-27. The Dry Truth: Hair-Wetting Improves Dry Electrode EEG Signal Quality. https://doi.org/10.64898/2026.09.21.753100

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