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Brain-computer interface (BCI) technology is an innovative human-computer interaction technology that does not rely on the peripheral nerve transmission pathway and muscle tissues, and establishes the connection between the human brain and the external machine. BCI system includes three categories: active, reactive and passive, with the motor imagery brain-computer interface (MI-BCI) is the most common active BCI system. MI-BCI controls external devices by imagining movements in the brain, without actually having to perform the movement. In order to bring more immersion to patients, the introduction of augmented reality (AR) technology can increase the interest of patients and improve the concentration on rehabilitation training. This study reviews the overview of BCI technology, the application of MI-BCI technology in the rehabilitation of nervous system diseases, and the limitations and prospects applications of MI-BCI technology in the rehabilitation of nervous system diseases, so as to provide reference for the application of MI-BCI technology in the diagnosis and rehabilitation of nervous system diseases. Specifically, the overview of BCI technology mainly introduces BCI technology, MI-BCI technology and AR-MI-BCI rehabilitation training system (the process and overall structure of the AR-MI-BCI rehabilitation training system). MI-BCI system has many applications in neurological diseases such as stroke, drug addiction and depression, which can not only effectively assist the diagnosis of neurological diseases, but also activate specific brain regions to promote brain function rehabilitation. MI-BCI system can identify the motor imagery intention of stroke patients and guide them to actively imagine body movements, which is helpful for active rehabilitation of patients. To address the poor generalization issues in traditional machine learning algorithms at the individual level of stroke patients, MI-BCI rehabilitation training system is built on the transfer learning technology. The rehabilitation training system based on AR-MI-BCI can assist drug users to reduce drug addiction and the difficult withdrawal of mental dependence and physical dependence caused by drug abuse, and make drug users have a connection between drugs and resistance emotion. EEG recognition scheme based on MI-BCI multi-frequency brain network can assist in the diagnosis of depression, but MI-BCI technology has some shortcomings in terms of technical development, device cost, patient privacy and clinical application. |
