Xu Kai, Wang Yueming, Zhang Shaomin, Zhao Ting, Wang Yiwen, Chen Weidong, Zheng Xiaoxiang
Qiushi Academy for Advanced Studies, Zhejiang University, Hangzhou 310027, PR China.
Annu Int Conf IEEE Eng Med Biol Soc. 2011;2011:4207-10. doi: 10.1109/IEMBS.2011.6091044.
Brain Machine Interfaces (BMI) aim at building a direct communication link between the neural system and external devices. The decoding of neuronal signals is one of the important steps in BMI systems. Existing decoding methods commonly fall into two categories, i.e., linear methods and nonlinear methods. This paper compares the performance between the two kinds of methods in the decoding of motor cortical activities of a monkey. Kalman filter (KF) is chosen as an example of linear methods, and General Regression Neural Network (GRNN) and Support Vector Regression (SVR) are two nonlinear approaches evaluated in our work. The experiments are conducted to reconstruct 2D trajectories in a center-out task. The correlation coefficient (CC) and the root mean square error (RMSE) are used to assess the performance. The experimental results show that GRNN and SVR achieve better performance than Kalman filter with average improvements of about 30% in CC and 40% in RMSE. This demonstrates that nonlinear models can better encode the relationship between the neuronal signals and response. In addition, GRNN and SVR are more effective than Kalman filter on noisy data.
脑机接口（BMI）旨在建立神经系统与外部设备之间的直接通信链路。神经元信号的解码是BMI系统的重要步骤之一。现有的解码方法通常分为两类，即线性方法和非线性方法。本文比较了这两种方法在猴子运动皮层活动解码中的性能。卡尔曼滤波器（KF）被选作线性方法的示例，广义回归神经网络（GRNN）和支持向量回归（SVR）是我们工作中评估的两种非线性方法。实验在中心向外任务中进行以重建二维轨迹。相关系数（CC）和均方根误差（RMSE）用于评估性能。实验结果表明，GRNN和SVR比卡尔曼滤波器具有更好的性能，CC平均提高约30%，RMSE平均提高约40%。这表明非线性模型能够更好地编码神经元信号与响应之间的关系。此外，在处理噪声数据时，GRNN和SVR比卡尔曼滤波器更有效。
