Brain-controlled wheelchair (BCW) is one of the important applications of brain-computer interface (BCI) technology. The present research shows that simulation control training is of great significance for the application of BCW. In order to improve the BCW control ability of users and promote the application of BCW under the condition of safety, this paper builds an indoor simulation training system based on the steady-state visual evoked potentials for BCW. The system includes visual stimulus paradigm design and implementation, electroencephalogram acquisition and processing, indoor simulation environment modeling, path planning, and simulation wheelchair control, etc. To test the performance of the system, a training experiment involving three kinds of indoor path-control tasks is designed and 10 subjects were recruited for the 5-day training experiment. By comparing the results before and after the training experiment, it was found that the average number of commands in Task 1, Task 2, and Task 3 decreased by 29.5%, 21.4%, and 25.4%, respectively (P < 0.001). And the average number of commands used by the subjects to complete all tasks decreased by 25.4% (P < 0.001). The experimental results show that the training of subjects through the indoor simulation training system built in this paper can improve their proficiency and efficiency of BCW control to a certain extent, which verifies the practicability of the system and provides an effective assistant method to promote the indoor application of BCW.
In order to explore the feasibility of applying magnetic detection electrical impedance tomography (MDEIT) on respiratory monitoring, aiming at the forward problem of magnetic detection electrical impedance tomography, we calculated the electric potential and current density distribution inside the imaging object by using the finite element method. We then got magnetic induction intensity outside the object at the end of exhaling and inhaling according to Biot-Savart's law. The results showed that the magnetic induction intensity at the end of inhaling was 8.875%, less than that at the end of exhaling. By the simulation results, we could understand the difference of magnetic induction intensity value surrounding the lung at the end of exhaling and inhaling due to the change of lung volume and electrical conductivity distribution better. Our research laid the foundation for the late image reconstruction and clinical disease detection.
