A flexible, stable, semi-dry electrode with low impedance for electroencephalography recording

用于脑电图记录的灵活、稳定、低阻抗半干电极

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作者:Yiyan Zhu, Caicaike Bayin, Hongjie Li, Xiaokang Shu, Jiangnan Deng, Haowen Yuan, Huyan Shen, Zhou Liang, Yao Li

Abstract

Brain-computer interfaces (BCIs) provide promising prospects for the field of healthcare and rehabilitation, presenting significant advantages for humanity. The development of electrodes that exhibit satisfactory performance characteristics, including high electrical conductivity, optimal comfort, and exceptional stability, is crucial for the effective implementation of electroencephalography (EEG) recording in BCI systems. The present study introduces a novel EEG electrode design that utilizes a composite material consisting of reduced graphene oxide (RGO) and polyurethane (PU) sponge. This electrode is characterized by its low impedance, stability, and flexibility. This work offers a high level of comfort while in touch with the skin and is designed to be user-friendly. Due to its notable moisturizing capacity, adaptable structure, and the presence of conductive RGO networks, the RGOPU semi-dry electrode exhibits a skin-contact impedance of less than 5.6 kΩ. This value is equivalent to that of a wet electrode and lower than that of a commercially available semi-dry electrode. The stability tests have demonstrated the outstanding electrical and mechanical performance of the material, hence confirming its suitability for long-term EEG recording. Additionally, the RGOPU semi-dry electrode demonstrates stable recording of EEG data and accurate detection of action potentials. Furthermore, the correlation coefficient between the RGOPU semi-dry electrode and wet electrodes exceeds 0.9. Additionally, it acquires electroencephalogram signals characterized by high signal-to-noise ratios (SNRs) in the context of alpha-wave and steady-state visual evoked potential (SSVEP) tests. The accuracy of the BCI is similar to that of wet electrodes, indicating a potential capability for sensing EEG in BCI applications.

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