Neural decoding based on probabilistic neural network.
Brain-machine interface (BMI) has been developed due to its possibility to cure severe body paralysis. This technology has been used to realize the direct control of prosthetic devices, such as robot arms, computer cursors, and paralyzed muscles. A variety of neural decoding algorithms have been designed to explore relationships between neural activities and movements of the limbs. In this paper, two novel neural decoding methods based on probabilistic neural network (PNN) in rats were introduced, the PNN decoder and the modified PNN (MPNN) decoder. In the experiment, rats were trained to obtain water by pressing a lever over a pressure threshold. Microelectrode array was implanted in the motor cortex to record neural activity, and pressure was recorded by a pressure sensor synchronously. After training, the pressure values were estimated from the neural signals by PNN and MPNN decoders. Their performances were evaluated by a correlation coefficient (CC) and a mean square error (MSE). The results show that the MPNN decoder, with a CC of 0.8657 and an MSE of 0.2563, outperformed the traditionally-used Wiener filter (WF) and Kalman filter (KF) decoders. It was also observed that the discretization level did not affect the MPNN performance, indicating that the MPNN decoder can handle different tasks in BMI system, including the detection of movement states and estimation of continuous kinematic parameters.
Yu
Yi
Y
Qiushi Academy for Advanced Studies, Zhejiang University, Hangzhou 310027, China.
Zhang
Shao-min
SM
Zhang
Huai-jian
HJ
Liu
Xiao-chun
XC
Zhang
Qiao-sheng
QS
Zheng
Xiao-xiang
XX
Dai
Jian-hua
JH
D000465:Algorithms; D000818:Animals; D004567:Electrodes, Implanted; D008328:Man-Machine Systems; D008959:Models, Neurological; D015233:Models, Statistical; D009044:Motor Cortex; D009068:Movement / Q000502:physiology; D016571:Neural Networks, Computer; D011312:Pressure; D011336:Probability; D051381:Rats; D017207:Rats, Sprague-Dawley; D015203:Reproducibility of Results; D014867:Water
D014867:Water