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

发布时间：2018-01-15 19:45

本文关键词：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　出处：《浙江大学》2014年博士论文　论文类型：学位论文

【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。
[Abstract]:The BCI system establishes a direct channel of interaction between the brain and external mechanical devices, which provides a new way for disabled patients to repair motor function, in which decoding algorithm is the core part of BCI system. The former decoding algorithms assume that the relationship between neuronal activity and motion expression is static and invariant. However, the study found that the regulation of spikes in neurons can be observed in short-term experiments. This paper analyzes the time-varying characteristics of motor cortical neurons on the implanted brain-computer interface platform based on rats and non-human primates. On this basis, a decoding algorithm which can track time-varying neural activity is designed to improve the accuracy of decoding and prolong the usage time of the model. In this paper, the experimental platform based on rat compression bar experiment and monkey two-dimensional arm movement was built. The neuroelectric signals and various motion parameters of primary motor cortex (M1) were collected simultaneously. Previous studies have qualitatively observed that the mode of neuronal spike release will change with time, and on this basis. In this paper, a time-varying generalized regression neural network algorithm based on black box model is proposed, which can absorb the new distribution mode and forget the old one. In order to dynamically track the time-varying activities of neurons, the coding mode of single neuron spike potential is further studied in this paper. The time-varying decoding algorithm of grey box model with physiological basis is designed. Firstly, a quantitative method of time-varying analysis of neuron coding function is established, and it is found that there are many coding forms in neurons. There is obvious time-varying phenomenon in the members and information of important subset of neuron, and a prediction method of time-varying law of coding function is established. In this paper, the time-varying property of neuron coding is incorporated into decoding algorithm. A double Monte Carlo point process filter is proposed, which is based on grey box model to track the time-varying rule of neural coding features, and performs better decoding performance in both simulation data and real data. In this study, quantitative analysis and decoding of the time-varying characteristics of motor cortical neurons in rats and monkeys have been carried out. The main innovation lies in. 1) the generalized regression neural network algorithm for dynamic growth of mode layer is designed to reduce the average error of decoded pressure signal in rat pressure-bar system. (2) the prediction method of neuron time-varying law based on linear-nonlinear Poisson coding model is established, which can better adapt to capture the diversity and time-varying of neuron coding. A dual Monte Carlo point process filtering method is proposed to dynamically analyze the time-varying activities of neuron clusters. The prediction error of monkey's two-dimensional rocker trajectory is reduced by more than 5%. In this study, a new way of quantificationally describing and analyzing the time-varying rule of neurons is explored to improve the decoding effect. The design of brain-computer interface system, which can work more stably, has laid the foundation.
【学位授予单位】：浙江大学
【学位级别】：博士
【学位授予年份】：2014
【分类号】：TN911.7

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