MXene-Enabled Self-Adaptive Hydrogel Interface for Active Electroencephalogram Interactions
Jiabei Luo, Chuanyue Sun, Boya Chang, Yangmin Jing, Kerui Li, Yaogang Li, Qinghong Zhang, Hongzhi Wang, Chengyi Hou
Abstract
Human–machine interaction plays a significant role in promoting convenience, production efficiency, and usage experience. Because of the universality and characteristics of electroencephalogram (EEG) signals, active EEG interaction is a promising and cutting-edge method for human–machine interaction. The seamless, skin-compliant, and motion-robust human–machine interface (HMI) for active EEG interaction has been in focus. Herein, we report a self-adaptive HMI (PAAS-MXene hydrogel) that can activate rapid gelation (5 s) using MXene cross-linking and conformably self-adapt to the scalp to help improve signal transduction. In addition to exhibiting satisfactory skin compliance, appropriate adhesion, and good biocompatibility, PAAS-MXene has demonstrated electrical performance reliability, such as low impedance (<50 Ω) at physiologically relevant frequencies, stable polarization potential (the rate of change is less than 6.5 × 10–4 V/min), negligible ion conductivity, and impedance change after 1000 stretch cycles, thereby realizing acquisition of EEG signals. In addition, a cap-free EEG signal acquisition method based on PAAS-MXene has been proposed. These findings confirm the high-precision detection ability of PAAS-MXene for electrocardiogram signals and EEG signals. Therefore, PAAS-MXene offers an option to actively control intention, motion, and vision through active EEG signals.