Study on a Novel Electrode With Automatic Replenishment of Conductive Fluid for Use in the Built Environment

There are some limitations in the use of traditional electroencephalogram (EEG) signal acquisition sensors. For example, the conductive paste of wet electrodes volatilizes faster and the operation process is cumbersome. However, the dry electrode has a large contact impedance value when it is used, which affects the quality of the collected signal. The traditional semi-dry electrode may cause a small movement of the electrode when the supplementary conductive solution is manually pressed, thus affecting the accuracy and stability of the signal. To address these issues, this study proposes a novel electrode that can automatically replenish the conductive liquid. The electrode can monitor the humidity of the electrode in real time through the humidity-sensitive material inside the electrode and realize the automatic supplementation of conductive liquid to the electrode. This kind of electrode improves the efficiency of the conductive liquid, increases the stability of the acquisition signal, and realizes the function of long-term uninterrupted acquisition of EEG signals. In addition, to solve the problem of time-consuming and labor-consuming lifting alignment caused by the uncoordinated communication between the tower crane driver and the lower preform lifting operator, this study proposes a method to improve the efficiency of preform lifting alignment based on the deep residual shrinkage network (DRSN). This method can effectively classify the motor imagery (MI) EEG signals of the preform hoisting operator, then convert the classification results into the instructions of the tower crane, and finally realize the accurate movement and alignment of the preform. The novel electrode can effectively collect EEG signals in the real building environment and prolong the effective use time compared with the traditional semi-dry electrode. The DRSN-based method shortens the time of preformed lifting and alignment and improves efficiency. Compared with other traditional methods, the DRSN algorithm performs better in terms of classification accuracy and anti-noise performance.