Hou Yao, Gu Zhenghui, Yu Zhu Liang, Xie Xiaofeng, Tang Rongnian, Xu Jinghan, Qi Feifei
Mechanical and Electrical Engineering College, Hainan University, Haikou, China.
College of Automation Science and Engineering, South China University of Technology, Guangzhou, China.
Front Hum Neurosci. 2022 Aug 11;16:975410. doi: 10.3389/fnhum.2022.975410. eCollection 2022.
Recently, motor imagery brain-computer interfaces (MI-BCIs) with stimulation systems have been developed in the field of motor function assistance and rehabilitation engineering. An efficient stimulation paradigm and Electroencephalogram (EEG) decoding method have been designed to enhance the performance of MI-BCI systems. Therefore, in this study, a multimodal dual-level stimulation paradigm is designed for lower-limb rehabilitation training, whereby visual and auditory stimulations act on the sensory organ while proprioceptive and functional electrical stimulations are provided to the lower limb. In addition, upper triangle filter bank sparse spatial pattern (UTFB-SSP) is proposed to automatically select the optimal frequency sub-bands related to desynchronization rhythm during enhanced imaginary movement to improve the decoding performance. The effectiveness of the proposed MI-BCI system is demonstrated on an the in-house experimental dataset and the BCI competition IV IIa dataset. The experimental results show that the proposed system can effectively enhance the MI performance by inducing the α, β and γ rhythms in lower-limb movement imagery tasks.
近年来,在运动功能辅助和康复工程领域,已开发出带有刺激系统的运动想象脑机接口(MI-BCI)。人们设计了一种高效的刺激范式和脑电图(EEG)解码方法,以提高MI-BCI系统的性能。因此,在本研究中,设计了一种用于下肢康复训练的多模态双水平刺激范式,视觉和听觉刺激作用于感觉器官,同时向下肢提供本体感觉和功能性电刺激。此外,还提出了上三角滤波器组稀疏空间模式(UTFB-SSP),以在增强想象运动期间自动选择与去同步节律相关的最佳频率子带,从而提高解码性能。在内部实验数据集和BCI竞赛IV IIa数据集上验证了所提出的MI-BCI系统的有效性。实验结果表明,所提出的系统能够通过在下肢运动想象任务中诱发α、β和γ节律来有效提高MI性能。