Preparation And Performance Of The Semi-invasive Brain-computer Interface Flexible Electrode Material With Magnetic Field Controlled Rheology

As an important branch of man-machine interaction,Brain-Computer Interface(BCI)has the potential to be widely used in various fields,such as medical health,drive control,aerospace,military and so on.The flexible electrode is the crucial component of the BCI,and is the key for the development of the BCI technology.According to the electrode position and implantation method,the BCI can be divided into three ways,including non-intrusive electrodes,intrusive electrodes and semi-intrusive electrodes.Compared with non-intrusive electrodes and intrusive electrodes,semi-invasive BCI electrodes could make a balance between the quality of electroencephalogram(EEG)signals and security.However,the application of the compressed mesh electrodes,the typical semi-invasive electrode,has been restricted due to the folds and tangles of the electrode material during injection and the insufficient deployment after injection.To solve the problem,we develop a novel semi-invasive BCI flexible electrode material based on Fe₃O₄@GO/P(NIPAM-MAA)hydrogel with magnetic field controlled rheology,which could break through the bottleneck problem restricting the development of BCI technology.Through the chemical co-precipitation method,the Fe₃O₄@GO disperse phase with conductive and magnetic was successfully prepared,which was combined with poly(N-isopropylacrylamide-methacrylic acid)to prepare Fe₃O₄@GO/P(NIPAM-MAA)hydrogel.The surface morphology and structure analysis showed that Fe₃O₄@GO/P(NIPAM-MAA)hydrogel formed an interpenetrating three-dimensional porous network structure,which not only provided an access channel that was beneficial to signals transmission,but also increased the contact area.In addition,the surface of the hole is rough,which is conducive to maintaining a good conformal contact between the hydrogel electrode and the brain tissue,thereby facilitating the stable collection of EEG signals.The temperature sensitivity test results illustrate that Fe₃O₄@GO/P(NIPAM-MAA)hydrogel can transform from sol to gel at body temperature,and its mechanical properties are similar to brain tissue.The electrical performance test results show that the resistivity of the Fe₃O₄@GO/P(NIPAM-MAA)hydrogel electrode is less than 120?·m,which makes it sensitive enough to transmit electrical signals between the brain and the computer,and has a large charge storage capacity and good electrochemical stability.The magnetic performance test results reveal that Fe₃O₄@GO/P(NIPAM-MAA)hydrogel has high saturation magnetization,hysteresis loop is not significant,which proves that it has superparamagnetism.The rheological test results describe that the Fe₃O₄@GO/P(NIPAM-MAA)hydrogel has good injectability and the gelation time is about 120s,which ensures a sufficient and safe magnetic control time window.The most special feature is that Fe₃O₄@GO/P(NIPAM-MAA)hydrogel has significant magnetorheological properties.The rheological behavior and the deformation can be remotely controlled by applying magnetic field during the gel process.The EEG signal acquisition test results outside the scalp demonstrate that the signal waveform and amplitude recorded by Fe₃O₄@GO/P(NIPAM-MAA)hydrogel electrode and wet electrode are basically the same,which proves that the prepared hydrogel electrode material can acquire and record effective EEG signals.As time went by,the Fe₃O₄@GO/P(NIPAM-MAA)hydrogel electrode was able to maintain good contact with the scalp,and the intrinsic impedance of the electrode and the quality of the recorded EEG signals did not change significantly.Compared with commercial wet electrodes,hydrogel electrodes exhibit higher average peak-to-peak values.Although the signal-to-noise ratio is similar between the Fe₃O₄@GO/P(NIPAM-MAA)hydrogel electrode and commercial wet electrodes,the hydrogel electrode is more stable,which proves that the Fe₃O₄@GO/P(NIPAM-MAA)hydrogel electrode can accurately collect and record EEG signals with a stable signal-to-noise ratio.