| Biotechnology |
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| Neural decoding based on probabilistic neural network |
| Yi Yu, Shao-min Zhang, Huai-jian Zhang, Xiao-chun Liu, Qiao-sheng Zhang, Xiao-xiang Zheng, Jian-hua Dai |
| 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 |
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Abstract 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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Received: 09 September 2009
Published: 29 March 2010
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