Motor Training and its Rehabilitation Application Based on the Neurofeedback Methods of Brain-Computer Interaction
He Feng1#, He Beibei1, Wang Zhongpeng1*, ChenLong2, Ming Dong1,2#
1(College of Precision Instruments and Optoelectronics Engineering, Tianjin University, Tianjin 300072, China) 2(Academy of Medical Engineering and Translational Medicine, Tianjin University, Tianjin 300072, China)
Abstract：Movement (or motor) is one of the most important forms of human survival, labour and communication with the outside world. However, some accidents or diseases can lead to the loss of partial or total motor functions of the human beings. Specifically, stroke has become the first major factor leading to the disability disease in China and the whole world. However, the conventional rehabilitation therapies are difficult to induce the synchronous coupling of corticomuscular function, especially the theoretical guidance of the mirror neuron system and neuroplasticity is lack, resulting in the limitation of the final rehabilitation effectiveness. Recently, the brain-computer interface (BCI) and/or other emerging human-machine technologies based on the neurofeedback training (NFT) method has emerged, which makes it quantitatively observable for the information of central nervous system and real-time perceptible for limb movement, thereby promoting functional reconstruction of the whole neural pathway and motor system. By summarizing the basic NFT principle of BCI training, combined with fusion and clinical application of current feedback training methods based on visual, auditory, tactile, and multi-sensory, it is expected that the future BCI training works in multi-sensory coordination and training mode becomes closed-loop controllable and adjustable based on the combination of initiative and external auxiliary function.
何峰, 何蓓蓓, 王仲朋, 陈龙, 明东. 脑-机交互运动训练的神经反馈方法及康复应用[J]. 中国生物医学工程学报, 2021, 40(6): 719-730.
He Feng, He Beibei, Wang Zhongpeng, ChenLong, Ming Dong. Motor Training and its Rehabilitation Application Based on the Neurofeedback Methods of Brain-Computer Interaction. Chinese Journal of Biomedical Engineering, 2021, 40(6): 719-730.
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