Title :
Comparisons between linear and nonlinear methods for decoding motor cortical activities of monkey
Author :
Xu, Kai ; Wang, Yueming ; Zhang, Shaomin ; Zhao, Ting ; Wang, Yiwen ; Chen, Weidong ; Zheng, Xiaoxiang
Author_Institution :
Qiushi Acad. for Adv. Studies, Zhejiang Univ., Hangzhou, China
fDate :
Aug. 30 2011-Sept. 3 2011
Abstract :
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.
Keywords :
Kalman filters; brain-computer interfaces; medical signal processing; neurophysiology; physiological models; regression analysis; signal reconstruction; support vector machines; 2D trajectories; Kalman filter; brain machine interfaces; center-out task; correlation coefficient; decoding motor cortical activities; external devices; general regression neural network; neural system; neuronal signals; nonlinear methods; root mean square error; support vector regression; Biological neural networks; Computational modeling; Decoding; Kalman filters; Support vector machines; Training; Trajectory; Animals; Macaca mulatta; Motor Cortex; Support Vector Machines;
Conference_Titel :
Engineering in Medicine and Biology Society, EMBC, 2011 Annual International Conference of the IEEE
Conference_Location :
Boston, MA
Print_ISBN :
978-1-4244-4121-1
Electronic_ISBN :
1557-170X
DOI :
10.1109/IEMBS.2011.6091044