Journal of Systems Engineering and Electronics ›› 2013, Vol. 24 ›› Issue (1): 157-164.doi: 10.1109/JSEE.2013.00020

• SOFTWARE ALGORITHM AND SIMULATION • Previous Articles     Next Articles

Efficient hybrid neural network for spike sorting

Hongge Li*, Pan Yu, and Tongsheng Xia   

  1. School of Electronics Information Engineering, Beihang University, Beijing 100191, China
  • Online:2013-02-25 Published:2010-01-03


Artificial neural network has been used successfully to develope the automatic spike extraction. In order to address some of the problems before the wireless transmission of the implantable chip, the automatic spike sorting method with low complexity and high efficiency is proposed based on the hybrid neural network with the principal component analysis network (PCAN) and normal boundary response (NBR) self-organizing mapping (SOM) network classifier. An automatic PCAN technique is used to reduce the dimension and eliminate the correlation of the spike signal. The NBR-SOM network performs the spike sorting challenge and improves the classification performance. The experimental results show that based on the hybrid neural network, the spike sorting method achieves the accuracy above 97.91% with signals containing five classes. The proposed NBR-SOM network classifier is to further improve the stability and effectiveness of the classification system.