植入式脑机接口神经元锋电位的时变特征分析与解码研究
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摘要
脑机接口系统在大脑与外部机械装置之间建立了一条直接交互的渠道,为残障病人修复运动功能提供新的方式。其中,解码算法是脑机接口系统的核心部分,承担着将神经信号准确翻译为运动指令的关键使命。以往的解码算法假设神经元活动与运动表达之间的联系是静态不变的,然而研究发现神经元的锋电位发放规律可在短期实验中观察到明显的变化,并导致解码效果逐渐下降。本文在基于大鼠和非人灵长类动物的植入式脑机接口平台上,分析运动皮层神经元编码特征的时变规律,并在此基础上设计能跟踪时变性神经活动的解码算法,用于提高解码准确性,延长模型的使用时间。
本文搭建了基于大鼠压杆实验和猴子二维手臂运动的实验平台,同步采集了初级运动皮层(M1)的神经电信号及多种运动参数。以往研究定性地观察到神经元锋电位的发放模式会随着时间变化,在此基础上,本文提出了基于黑盒模型的时变广义回归神经网络算法。该方法能不断吸收新出现的发放模式,·忘记不再出现的旧模式,从而动态实现对神经元时变活动的跟踪。本文进一步研究了单个神经元锋电位的编码模态,设计了具有生理基础的灰盒模型时变解码算法。首先建立了神经元编码函数时变分析的定量方法,发现神经元存在多种编码形式；神经元重要子集的成员和信息量都存在明显的时变现象,并建立了编码函数时变规律的预测方法。本文将神经元编码的时变性质融入解码算法中,提出了双重蒙特卡罗点过程滤波器。这种基于灰盒模型的算法能跟踪神经元编码特征的时变规律,在仿真数据和真实数据上实验都表现出更好的解码效果。
本研究工作实现了大鼠及猴子运动皮层神经元编码特征时变规律的定量分析和解码研究,主要创新点在于,(1)设计模式层动态增长的广义回归神经网络算法,降低了大鼠压杆系统中解码压力信号的平均误差；(2)建立了基于线性-非线性-泊松编码模型的神经元时变规律的预测方法,能够更好地适应捕捉神经元编码的多样性和时变性；(3)提出融入神经元编码特性的双重蒙特卡罗点过程滤波方法,用于动态解析神经元集群的时变活动,将猴子二维摇杆的轨迹预测误差降低5%以上。本研究探索了一条定量描述和解析神经元时变规律的新思路,为提高解码效果,设计能更稳定工作的脑机接口系统奠定了基础。
Brain machine interfaces (BMIs) build a direct pathway for the communication between the brain and the external instruments, aiming to help restoring motor function for motor-impaired patients. The decoding algorithm is the core of BMIs, which translates the neural activity into behavior. Previous methods usually assume a static functional map between neural firing and movement. However, recent work indicates the significant time variance in neural activities during short experiments, which greatly reduces the decoding performance. Based on the invasive BMIs on rats and non-human primates, we analyze the time-varying neural encoding characteristics and integerate it into the design of the non-stationary decoding algorithms, which makes the prediction more accuracy and more stable.
We establish the invasive BMI platform on rats performning lever-pressing task and non-human primates performing2D tracking task. Neural activities are collected from primary motor cortex and syncrized with the movement. As the firing patterns have been qualitatively proved to be time-varying, we develope a time-varying general regression neural network (GRNN) as a black box to decode the neural firings. The model is designed with a dynamic pattern layer, which can take in the new patterns and forget the old ones in time. Therefore it can follow the change of firing patterns. Futhermore, we analysis the tuning characteristic of single unit activity, and design a time-varying decoding algorithm as a grey box that involes more physiological information. First, we estimate the time-variant tuning curves in a data-driven way and find both functionally "inhibitory" and the traditional "excitatory" function neurons. Then, we find worthy noticing changes in both information amount and member of the important neuron subset. Also, we propose a random walk model to predict time-variant tuning curves. Based on these time-varying neural tuning analyses, we propose a dual Monte Carlo point process filter (MCPP). The grey box method enables the estimation on the dynamic tuning parameters. When applied on both simulated neural signal and in vivo BMI data, the proposed adaptive method performs better than the one with static tuning model.
This study first evaluates the non-stationary neural activity of M1in rats and monkey quantitatively. The innovations are,(i) desiging a time-varying GRNN with a dynamic pattern layer. When applied to pressure prediction in rats'data, the mean prediction error decreases;(ii) establishing a parameteric way to predict time-varing neural activity based on the linear-nonlinear-posisson model, which captures multiple neural tuning propeties;(iii) proposing a dual MCPP method with updated tuning models. When applied to trajractory estimation of monkeys, the normalized mean square error decreases over5%. Overall, our results raise a promising way to design a long-term-performing model for BMI decoder.
本文搭建了基于大鼠压杆实验和猴子二维手臂运动的实验平台,同步采集了初级运动皮层(M1)的神经电信号及多种运动参数。以往研究定性地观察到神经元锋电位的发放模式会随着时间变化,在此基础上,本文提出了基于黑盒模型的时变广义回归神经网络算法。该方法能不断吸收新出现的发放模式,·忘记不再出现的旧模式,从而动态实现对神经元时变活动的跟踪。本文进一步研究了单个神经元锋电位的编码模态,设计了具有生理基础的灰盒模型时变解码算法。首先建立了神经元编码函数时变分析的定量方法,发现神经元存在多种编码形式；神经元重要子集的成员和信息量都存在明显的时变现象,并建立了编码函数时变规律的预测方法。本文将神经元编码的时变性质融入解码算法中,提出了双重蒙特卡罗点过程滤波器。这种基于灰盒模型的算法能跟踪神经元编码特征的时变规律,在仿真数据和真实数据上实验都表现出更好的解码效果。
本研究工作实现了大鼠及猴子运动皮层神经元编码特征时变规律的定量分析和解码研究,主要创新点在于,(1)设计模式层动态增长的广义回归神经网络算法,降低了大鼠压杆系统中解码压力信号的平均误差；(2)建立了基于线性-非线性-泊松编码模型的神经元时变规律的预测方法,能够更好地适应捕捉神经元编码的多样性和时变性；(3)提出融入神经元编码特性的双重蒙特卡罗点过程滤波方法,用于动态解析神经元集群的时变活动,将猴子二维摇杆的轨迹预测误差降低5%以上。本研究探索了一条定量描述和解析神经元时变规律的新思路,为提高解码效果,设计能更稳定工作的脑机接口系统奠定了基础。
Brain machine interfaces (BMIs) build a direct pathway for the communication between the brain and the external instruments, aiming to help restoring motor function for motor-impaired patients. The decoding algorithm is the core of BMIs, which translates the neural activity into behavior. Previous methods usually assume a static functional map between neural firing and movement. However, recent work indicates the significant time variance in neural activities during short experiments, which greatly reduces the decoding performance. Based on the invasive BMIs on rats and non-human primates, we analyze the time-varying neural encoding characteristics and integerate it into the design of the non-stationary decoding algorithms, which makes the prediction more accuracy and more stable.
We establish the invasive BMI platform on rats performning lever-pressing task and non-human primates performing2D tracking task. Neural activities are collected from primary motor cortex and syncrized with the movement. As the firing patterns have been qualitatively proved to be time-varying, we develope a time-varying general regression neural network (GRNN) as a black box to decode the neural firings. The model is designed with a dynamic pattern layer, which can take in the new patterns and forget the old ones in time. Therefore it can follow the change of firing patterns. Futhermore, we analysis the tuning characteristic of single unit activity, and design a time-varying decoding algorithm as a grey box that involes more physiological information. First, we estimate the time-variant tuning curves in a data-driven way and find both functionally "inhibitory" and the traditional "excitatory" function neurons. Then, we find worthy noticing changes in both information amount and member of the important neuron subset. Also, we propose a random walk model to predict time-variant tuning curves. Based on these time-varying neural tuning analyses, we propose a dual Monte Carlo point process filter (MCPP). The grey box method enables the estimation on the dynamic tuning parameters. When applied on both simulated neural signal and in vivo BMI data, the proposed adaptive method performs better than the one with static tuning model.
This study first evaluates the non-stationary neural activity of M1in rats and monkey quantitatively. The innovations are,(i) desiging a time-varying GRNN with a dynamic pattern layer. When applied to pressure prediction in rats'data, the mean prediction error decreases;(ii) establishing a parameteric way to predict time-varing neural activity based on the linear-nonlinear-posisson model, which captures multiple neural tuning propeties;(iii) proposing a dual MCPP method with updated tuning models. When applied to trajractory estimation of monkeys, the normalized mean square error decreases over5%. Overall, our results raise a promising way to design a long-term-performing model for BMI decoder.
引文
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