中文摘要：

针对大脑运动皮层群体神经元信号与运动轨迹关系的辨识，分别建立了基于人工神经网络（ANN）和基于最小二乘支持向量机（LS-SVM）的非线性具有外部输入的自回归NARX模型．在三维虚拟空间中对猴子手臂运动实验记录的多通道神经元信号进行分析，通过与线性ARX模型的比较，说明非线性模型比线性模型能够更好地描述脑运动神经系统，并且用最小二乘支持向量机建立的模型比人工神经网络建立模型的预测精度更高，泛化能力更强，适用于大脑皮层神经元信号的分析，有利于实现性能更高的脑机接口系统．

英文摘要：

Nonlinear autoregressive with exogenous （NARX） models based artificial neural networks （ANN） and least squares support vector machines （LS-SVM） were established to identify the relations between the activities of cortical neural ensemble and movement trajectories. The populations of neurons were recorded simultaneously with kinematics of arm movement while the monkey performed center-out task in a three-dimensional virtual environment. The results show that the nonlinear NARX method is better than the linear ARX method to model the cortical neural system. And the LS- SVM based model has higher prediction accuracy and better generalization performance than that of ANN based model. It is seen that the LS-SVM algorithm is suitable for cortical signals analyses and holds hope for a possible more accurate brain-computer interface （BCI）.