An EEG-driven Lower Limb Rehabilitation Training System for Active and Passive Co-stimulation

Abstract

With the advent of an aging society, stroke makes a heavy burden for our society. Stroke can damage the motor and sensory neural system and block the closed loop between the brain and the body. Due to the neural plasticity, this closed loop can be rebuilt through training. Users' actively engagement can help expedite functional recovery. Therefore, we propose an EEG-driven Lower Limb Rehabilitation Training System (LLRTS) that can achieve Active and Passive Co-stimulation (APC). Virtual Reality (VR), BCI and robot are introduced into the system. When users have motor intentions, this system could automatically support corresponding visual and somatic sensory feedback. That is to say active and passive stimulations are controlled by user's mind. This paper reports the idea and construction of this rehabilitation system. Preliminary experimental results support the concept.

A Light Spot Humanoid Motion Paradigm Modulated by the Change of Brightness to Recognize the Stride Motion Frequency

“…Finally, based on the EEG-driven lower limb rehabilitation training system, which we proposed in our previous study ( Zhang et al, 2015 ), we applied the designed paradigm to control a robot by EEG with the same frequency stimulation. Even though in our previous study ( Zhang et al, 2015 ) the virtual reality was designed to provide visual feedback, the stimulus was the normal SSMVEP paradigm. Accordingly, subjects could not know the meaning of the paradigm if the experimenter did not tell them.…”

“…The XYKXZFK-9 lower limb robot (Xiangyu Medical Equipment Co. Ltd., China) was chosen to obtain motor feedback. This robot had been described in our previous research ( Zhang et al, 2015 ). The robot was able to drive the lower extremity of users with reciprocating exercise similar to treadmill exercise.…”

“…Then, the accuracies of distinguishing the different stride frequencies with the same and different high frequencies were compared. Finally, based on our previous rehabilitation training robot ( Zhang et al, 2015 ), we employed the designed paradigm to control a lower limb robot with the same frequency stimulation. The robot provided motor feedback according to the identification of the electroencephalograph (EEG) signals when subjects stared at the humanoid motion paradigms with different stride frequencies.…”

Analysis of the EEG Rhythms Based on the Empirical Mode Decomposition During Motor Imagery When Using a Lower-Limb Exoskeleton. A Case Study

“…Steady-State Visual Evoked Potentials (SSVEPs) are related to the response offered by the brain due to an external stimulus. Its nature is not directly related to the movement or MI, but this response can be used to control an exoskeleton with a BMI (Kwak et al, 2015 ; Zhang et al, 2015 ; Gui et al, 2017 ). Although the potential is not related to a movement action, it can be used to train the user to make a connection between the visual stimulus and the desired robotic action.…”

Interactive control methods for rehabilitation robot

“…为充分利用脑电信号中的时序特征及各通道之间的空间特征, 采 用三维 Spiking 神经网络为核心的分类方法, 得到了较好的分类结果, 为直接提取患者运动意图打下 基础.由于运动神经中枢损坏的脑卒中患者难以有效完成运动感知与运动控制. 对于脑部具有正常视觉 通路与清晰的思维意识的脑卒中患者, Zhang 等[85] 提出基于脑电信号驱动的融合虚拟现实、脑机接 口和机器人的下肢康复训练系统, 该系统能够主被动联合激励患者. 其中, 基于镜像神经元理论, 虚拟 现实可以增强运动神经中枢的视觉刺激, 视觉刺激通过镜像神经元主动刺激运动控制神经元; 机器人 被动刺激运动知觉神经元; 脑电信号检测受试者运动意图, 控制主被动联合刺激.…”