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CLC number: TP2; R741

On-line Access: 2010-03-29

Received: 2009-09-09

Revision Accepted: 2010-01-23

Crosschecked: 2010-03-15

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Journal of Zhejiang University SCIENCE B 2010 Vol.11 No.4 P.298-306


Neural decoding based on probabilistic neural network

Author(s):  Yi Yu, Shao-min Zhang, Huai-jian Zhang, Xiao-chun Liu, Qiao-sheng Zhang, Xiao-xiang Zheng, Jian-hua Dai

Affiliation(s):  Qiushi Academy for Advanced Studies, Zhejiang University, Hangzhou 310027, China, College of Biomedical Engineering and Instrument Science, Zhejiang University, Hangzhou 310027, China, Key Laboratory of Biomedical Engineering of Ministry of Education, Zhejiang University, Hangzhou 310027, China, College of Computer Science and Technology, Zhejiang University, Hangzhou 310027, China

Corresponding email(s):   qaas@zju.edu.cn

Key Words:  Brain-machine interfaces (BMI), Neural decoding, Probabilistic neural network (PNN), Microelectrode array

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Yi Yu, Shao-min Zhang, Huai-jian Zhang, Xiao-chun Liu, Qiao-sheng Zhang, Xiao-xiang Zheng, Jian-hua Dai. Neural decoding based on probabilistic neural network[J]. Journal of Zhejiang University Science B, 2010, 11(4): 298-306.

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author="Yi Yu, Shao-min Zhang, Huai-jian Zhang, Xiao-chun Liu, Qiao-sheng Zhang, Xiao-xiang Zheng, Jian-hua Dai",
journal="Journal of Zhejiang University Science B",
publisher="Zhejiang University Press & Springer",

%0 Journal Article
%T Neural decoding based on probabilistic neural network
%A Yi Yu
%A Shao-min Zhang
%A Huai-jian Zhang
%A Xiao-chun Liu
%A Qiao-sheng Zhang
%A Xiao-xiang Zheng
%A Jian-hua Dai
%J Journal of Zhejiang University SCIENCE B
%V 11
%N 4
%P 298-306
%@ 1673-1581
%D 2010
%I Zhejiang University Press & Springer
%DOI 10.1631/jzus.B0900284

T1 - Neural decoding based on probabilistic neural network
A1 - Yi Yu
A1 - Shao-min Zhang
A1 - Huai-jian Zhang
A1 - Xiao-chun Liu
A1 - Qiao-sheng Zhang
A1 - Xiao-xiang Zheng
A1 - Jian-hua Dai
J0 - Journal of Zhejiang University Science B
VL - 11
IS - 4
SP - 298
EP - 306
%@ 1673-1581
Y1 - 2010
PB - Zhejiang University Press & Springer
ER -
DOI - 10.1631/jzus.B0900284

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.

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