Evaluation of Pre-Applied Conductive Materials in Electrode Grids for Longterm EEG Recording

Most long-term mobile EEG monitoring systems require professional application of the electrodes, which makes them inconvenient for everyday use. Additionally, many materials that facilitate EEG application, such as dry electrodes, may cause discomfort when worn for longer periods of time. To address these problems, we designed flex-printed EEG electrode grids (trEEGrid) and evaluated signal quality based on two pre-applied conductive materials. Self-applicable trEEGrid patches with a conductive solid hydrogel and a novel silicone-based dry material were used in a day-long (5-6 h) recording session, which included a 4 h continuous recording of impedance levels, as well as two auditory task recordings in the morning and afternoon. The signal-to-noise ratio (SNR) of the auditory evoked potentials (AEPs), AEP morphology, and impedance levels of the conductive materials were compared to evaluate overall signal quality, and further comparisons took place between the morning and afternoon sessions to evaluate signal deterioration over time. Comparable impedance values were observed for both silicone and hydrogel materials, but the silicone material exhibited a higher outlier rate, with impedance values over 200 kΩ. Over time, the impedance values increased for the silicone material and decreased for the hydrogel material. The morphology of the AEP was reproduced comparably well with both materials, with reasonable SNRs in both the morning and the afternoon. In conclusion, when combined with flex-printed electrode grids, silicone and hydrogel materials make it feasible to collect high-quality long-term EEG signals with high wearing comfort.

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Publication Details

Journal
Sensors
Published
2025-11-07
DOI
https://doi.org/10.3390/s25226810
Citations
1
Primary Topic
EEG and Brain-Computer Interfaces
Type
article
Field-Weighted Citation Impact
1.43
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article

Evaluation of Pre-Applied Conductive Materials in Electrode Grids for Longterm EEG Recording

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1 citations
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EEG and Brain-Computer Interfaces
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article

Evaluation of Pre-Applied Conductive Materials in Electrode Grids for Longterm EEG Recording

Wiebke Pätzold, Joanna E. M. Scanlon, Karen Insa Wolf, Stefan Debener, Axel H. Winneke, Carlos F. da Silva Souto
article en
1 citations

Abstract

Most long-term mobile EEG monitoring systems require professional application of the electrodes, which makes them inconvenient for everyday use. Additionally, many materials that facilitate EEG application, such as dry electrodes, may cause discomfort when worn for longer periods of time. To address these problems, we designed flex-printed EEG electrode grids (trEEGrid) and evaluated signal quality based on two pre-applied conductive materials. Self-applicable trEEGrid patches with a conductive solid hydrogel and a novel silicone-based dry material were used in a day-long (5-6 h) recording session, which included a 4 h continuous recording of impedance levels, as well as two auditory task recordings in the morning and afternoon. The signal-to-noise ratio (SNR) of the auditory evoked potentials (AEPs), AEP morphology, and impedance levels of the conductive materials were compared to evaluate overall signal quality, and further comparisons took place between the morning and afternoon sessions to evaluate signal deterioration over time. Comparable impedance values were observed for both silicone and hydrogel materials, but the silicone material exhibited a higher outlier rate, with impedance values over 200 kΩ. Over time, the impedance values increased for the silicone material and decreased for the hydrogel material. The morphology of the AEP was reproduced comparably well with both materials, with reasonable SNRs in both the morning and the afternoon. In conclusion, when combined with flex-printed electrode grids, silicone and hydrogel materials make it feasible to collect high-quality long-term EEG signals with high wearing comfort.

SensorsVol. 25(22)
Carl von Ossietzky Universität Oldenburg (DE), Fraunhofer Institute for Digital Media Technology (DE), Hearing4all (DE)
Openalex Percentile: Top 14%
EEG and Brain-Computer Interfaces
1.43
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