4.8 Article

Boron and nitrogen co-doped vertical graphene electrodes for scalp electroencephalogram recording

Journal

CARBON
Volume 189, Issue -, Pages 71-80

Publisher

PERGAMON-ELSEVIER SCIENCE LTD
DOI: 10.1016/j.carbon.2021.12.056

Keywords

Electroencephalogram; Boron and nitrogen co-doped vertical graphene; CVD; Steady-state visual evoked potentials

Funding

  1. Natural Science Foundation of Tianjin City [20JCZDJC00290]

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The study demonstrates that B and N co-doped vertical graphene (BNVG) electrodes can improve the contact issue of graphene in monitoring scalp electroencephalogram (EEG) signals. By analyzing the effects of different B and N atom contents, the optimal BNVG electrode is determined and successfully applied in an EEG cap for recording EEG signals, which has important implications for clinical and brain-computer interface systems.
Graphene has shown immense potential for applications in monitoring scalp electroencephalogram (EEG) signals. However, the radial head shape, high-resistance scalp cuticle, and hair prevent graphene from contacting skin, resulting in high contact impedances and low signal-to-noise ratios (SNRs) in EEG signals. Therefore, B and N co-doped vertical graphene (BNVG) electrodes are synthesized to improve the skin affinity and sweat adsorption capacity. X-ray photoelectron spectroscopy results show that the series of BNVG electrodes comprise B-doping contents of 1.25-9.85 at% and N-doping contents of 1.12-6.48 at%. A systematic analysis of the effects of B and N atom contents on the real-time scalp-contact resistances, EEG correlation coefficients between BNVG and Ag/AgCl electrodes, and SNRs of the EEG signals are conducted. The BNVG electrode comprising 4.47 at% B and 3.18 at% N is determined as the optimum electrode for scalp EEG signal acquisition. Furthermore, an EEG cap assembled with 19 optimized BNVG electrodes records spontaneous and evoked EEG signals. The BNVG-based EEG cap used with sweat or aqueous NaCl solution is confirmed to be advantageous and can be applied to clinical and brain-computer interface systems. (c) 2021 Elsevier Ltd. All rights reserved.

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