BCICFES重建运动神经系统的信号处理与控制关键技术研究
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摘要
功能性电刺激(Functional Electrical Stimulation, FES)技术可帮助脊髓损伤或肢体运动功能缺失患者重建相应的外周神经系统、恢复或改善肌肉动作功能,是目前神经工程研究与康复医学应用的前沿领域与重要发展方向之一。但至今FES仍停留在患肢末端神经刺激局部作用模式,不能听从患者主观运动意愿控制,使其自适应性差、易受干扰、难以学习掌握,严重制约了FES的治疗效果,成为亟待解决的技术瓶颈问题。
近年来兴起的脑-机接口(Brain-Computer Interface,BCI)技术使FES按照患者主观运动意识控制的构想成为可能。通过BCI监测、识别患者主观肢体运动意识,以此产生相应的FES控制信号模式,实现患者自觉运动康复训练。这种BCI控制FES(BCI Controls FES, BCICFES)的全新运动神经重建技术引导了当前神经康复工程研究的新潮流。本论文针对BCICFES重建运动神经系统中BCI数据去噪、特征提取、主观运动模式识别和FES脉冲强度、触发时序、反馈模型等信号处理与控制关键技术进行了较详细的研究。在运用极能差(Extreme Energy Difference, EED)、共空间模式(Common Spatial Pattern, CSP)滤波手段进行脑电思维信息空间解码;利用高频段肌电能量估算BCI脑电数据中低频段的肌电干扰并实时去噪;采用误差反向传递(Back Propagation, BP)和径向基函数(Radial Basis Function, RBF)人工神经网络构成自适应比例微积分(PID)控制器用于动态控制FES刺激模式及强度等方面提出了具有一定创新特色的方法,可为BCICFES运动神经重建系统的实现提供较关键的技术支撑。
本课题设计并完成了不同侧向性的上肢想象动作诱发脑电实验,经六名受试者的BCICFES实验证明:利用CSP模式滤波与递归信号筛选组合方法可以加强信号的空间区域特征、优化筛选时-频特征维数,取得了平均识别率86.2%(最好达92.8%)的分类结果;高频校正算法可以实时去除BCI数据中低频段的肌电噪声,并保留其有用的脑电信息;两种人工神经网络算法自适应整定PID控制器可根据实际输出与预设轨迹偏差在线优化PID控制器的比例、积分和微分系数,实现对FES装置的自适应动态控制,明显降低超调量、提高动作精度。
研究结果表明,上述关键技术能够有效地提高BCICFES运动神经重建系统对患者主观运动意愿的预测识别能力,增强系统的自适应与抗干扰性能,为实现患者自主运动康复、提高训练自理能力、达到理想治疗效果提供了较好的研究开发基础。
Functional Electrical Stimulation (FES), one of the most frontier areas and direction in neuron engineering research and rehabilitation Medicine, reconstructs the peripheral nervous system externally and restores the muscular motor function for patients with paralyzed limb result from spinal cord injury. However, lack of convenient approach to transmit subjective intention of patients to control the FES device directly restricts the application of FES seriously that make it a technical bottleneck for further popularization as its complex, bad self adaptability and interfered easily.
Brain-Computer Interface (BCI) as a rising technique developed in recent years makes it possible for patients with paralyzed limbs to control the FES device by the their intention personally. It can be a new trend in neural rehabilitation engineering research to reconstruct the peripheral nervous system by BCICFES which control FES for rehabilitation training by monitoring the movement intention using BCI technique. This paper focuses on the key techniques of signal processing and control in motor neural system reconstruction by BCICFES, including BCI denoising method, feature extraction algorithm, pattern recognition as well as the stimulation pattern and feedback control stratagem of FES system. Decoding the motor intention spatially by Extreme Energy Difference (EED) and Common Spatial Pattern (CSP), eliminating scalp EMG contamination from EEG signals by using power changes in the higher frequency bands to estimate and remove EMG contamination in the lower frequency bands, and building FES feedback control system by adaptive PID controller that modulated by Back Propagation (BP) and Radial Basis Function (RBF) Neural Network, can provide worthy technical support for the construction of BCICFES external neural system.
In this study an evoked EEG experiment on different imaginary tasks of upper extremity was designed and operated, the experimental results indicated that the best rate of accuracy which gotten from CSP and RFE could be 92.86% for all the six subjects and the averaged accuracy rate was 86.22%. The proposed EMG correction method was proved a successful method for EMG noise removal while did not washed away any true EEG information. The adaptive PID controllers modulated by BP and RBF Neural Network enhanced control precision of knee joint angle significantly in tracing tasks for FES system, and achieve satisfy effect on overshoot and oscillation suppression.
The results showed that the key techniques described above improved the recoginition ability for the BCICFES external neural system to predict the motor intention of the subjects and increased its capability of self adaptive and interference suppression, which were proved worthy technique supports for further development. This research laid a solid foundation for accomplishing voluntary movement rehabilitation, enhancing independent ability and achieving a satisfying treatment for the patients.
近年来兴起的脑-机接口(Brain-Computer Interface,BCI)技术使FES按照患者主观运动意识控制的构想成为可能。通过BCI监测、识别患者主观肢体运动意识,以此产生相应的FES控制信号模式,实现患者自觉运动康复训练。这种BCI控制FES(BCI Controls FES, BCICFES)的全新运动神经重建技术引导了当前神经康复工程研究的新潮流。本论文针对BCICFES重建运动神经系统中BCI数据去噪、特征提取、主观运动模式识别和FES脉冲强度、触发时序、反馈模型等信号处理与控制关键技术进行了较详细的研究。在运用极能差(Extreme Energy Difference, EED)、共空间模式(Common Spatial Pattern, CSP)滤波手段进行脑电思维信息空间解码;利用高频段肌电能量估算BCI脑电数据中低频段的肌电干扰并实时去噪;采用误差反向传递(Back Propagation, BP)和径向基函数(Radial Basis Function, RBF)人工神经网络构成自适应比例微积分(PID)控制器用于动态控制FES刺激模式及强度等方面提出了具有一定创新特色的方法,可为BCICFES运动神经重建系统的实现提供较关键的技术支撑。
本课题设计并完成了不同侧向性的上肢想象动作诱发脑电实验,经六名受试者的BCICFES实验证明:利用CSP模式滤波与递归信号筛选组合方法可以加强信号的空间区域特征、优化筛选时-频特征维数,取得了平均识别率86.2%(最好达92.8%)的分类结果;高频校正算法可以实时去除BCI数据中低频段的肌电噪声,并保留其有用的脑电信息;两种人工神经网络算法自适应整定PID控制器可根据实际输出与预设轨迹偏差在线优化PID控制器的比例、积分和微分系数,实现对FES装置的自适应动态控制,明显降低超调量、提高动作精度。
研究结果表明,上述关键技术能够有效地提高BCICFES运动神经重建系统对患者主观运动意愿的预测识别能力,增强系统的自适应与抗干扰性能,为实现患者自主运动康复、提高训练自理能力、达到理想治疗效果提供了较好的研究开发基础。
Functional Electrical Stimulation (FES), one of the most frontier areas and direction in neuron engineering research and rehabilitation Medicine, reconstructs the peripheral nervous system externally and restores the muscular motor function for patients with paralyzed limb result from spinal cord injury. However, lack of convenient approach to transmit subjective intention of patients to control the FES device directly restricts the application of FES seriously that make it a technical bottleneck for further popularization as its complex, bad self adaptability and interfered easily.
Brain-Computer Interface (BCI) as a rising technique developed in recent years makes it possible for patients with paralyzed limbs to control the FES device by the their intention personally. It can be a new trend in neural rehabilitation engineering research to reconstruct the peripheral nervous system by BCICFES which control FES for rehabilitation training by monitoring the movement intention using BCI technique. This paper focuses on the key techniques of signal processing and control in motor neural system reconstruction by BCICFES, including BCI denoising method, feature extraction algorithm, pattern recognition as well as the stimulation pattern and feedback control stratagem of FES system. Decoding the motor intention spatially by Extreme Energy Difference (EED) and Common Spatial Pattern (CSP), eliminating scalp EMG contamination from EEG signals by using power changes in the higher frequency bands to estimate and remove EMG contamination in the lower frequency bands, and building FES feedback control system by adaptive PID controller that modulated by Back Propagation (BP) and Radial Basis Function (RBF) Neural Network, can provide worthy technical support for the construction of BCICFES external neural system.
In this study an evoked EEG experiment on different imaginary tasks of upper extremity was designed and operated, the experimental results indicated that the best rate of accuracy which gotten from CSP and RFE could be 92.86% for all the six subjects and the averaged accuracy rate was 86.22%. The proposed EMG correction method was proved a successful method for EMG noise removal while did not washed away any true EEG information. The adaptive PID controllers modulated by BP and RBF Neural Network enhanced control precision of knee joint angle significantly in tracing tasks for FES system, and achieve satisfy effect on overshoot and oscillation suppression.
The results showed that the key techniques described above improved the recoginition ability for the BCICFES external neural system to predict the motor intention of the subjects and increased its capability of self adaptive and interference suppression, which were proved worthy technique supports for further development. This research laid a solid foundation for accomplishing voluntary movement rehabilitation, enhancing independent ability and achieving a satisfying treatment for the patients.
引文
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