Feature reconstruction of LFP signals based on PLSR in the neural information decoding study

To solve the problems of Signal-to-Noise Ratio (SNR) and multicollinearity when the Local Field Potential (LFP) signals is used for the decoding of animal motion intention, a feature reconstruction of LFP signals based on partial least squares regression (PLSR) in the neural information decoding study is proposed in this paper. Firstly, the feature information of LFP coding band is extracted based on wavelet transform. Then the PLSR model is constructed by the extracted LFP coding features. According to the multicollinearity characteristics among the coding features, several latent variables which contribute greatly to the steering behavior are obtained, and the new LFP coding features are reconstructed. Finally, the K-Nearest Neighbor (KNN) method is used to classify the reconstructed coding features to verify the decoding performance. The results show that the proposed method can achieve the highest accuracy compared to the other three methods and the decoding effect of the proposed method is robust.

[1]  Zhiqiang Ge,et al.  Probabilistic learning of partial least squares regression model: Theory and industrial applications , 2016 .

[2]  Bo Jiang,et al.  Neural decoding using local field potential based on partial least squares regression , 2011, 2011 Annual International Conference of the IEEE Engineering in Medicine and Biology Society.

[3]  RENHUAN YANG,et al.  Feature Extraction of Motor Imagery EEG Based on Wavelet Transform and Higher-Order Statistics , 2010, Int. J. Wavelets Multiresolution Inf. Process..

[4]  Alejandro F. Frangi,et al.  A Predictive Model of Vertebral Trabecular Anisotropy From Ex Vivo Micro-CT , 2015, IEEE Transactions on Medical Imaging.

[5]  Xavier Maldague,et al.  Optimization of pulsed thermography inspection by partial least-squares regression , 2014 .

[6]  H. Abdi.  Partial least squares regression and projection on latent structure regression (PLS Regression) , 2010 .

[7]  G. G. Stokes.  "J." , 1890, The New Yale Book of Quotations.

[8]  Xiaochun Liu,et al.  Neural Ensemble Decoding of Rat's Motor Cortex by Kalman Filter and Optimal Linear Estimation , 2009, 2009 2nd International Congress on Image and Signal Processing.

[9]  Wang Wei-xiong.  The Filtering Methods of Bowel Sounds in Wavelet Domain , 2011 .

[10]  Quan Pan,et al.  Two denoising methods by wavelet transform , 1999, IEEE Trans. Signal Process..

[11]  Ni Yuan.  Research of weak life signals’extraction based on wavelet transform , 2009 .

[12]  Andreas Nieder,et al.  Neuronal Correlates of Visual Working Memory in the Corvid Endbrain , 2014, The Journal of Neuroscience.

[13]  Yaxin Bi,et al.  KNN Model-Based Approach in Classification , 2003, OTM.

[14]  T. Mehmood.  Hotelling T2 based variable selection in partial least squares regression , 2016 .

[15]  Chun Chen,et al.  EEG Detection Based on Wavelet Transform and SVM Method , 2016, 2016 IEEE International Conference on Smart Cloud (SmartCloud).

[16]  Jizhen Liu,et al.  A novel least squares support vector machine ensemble model for NOx emission prediction of a coal-fired boiler , 2013 .

[17]  F. Mussa-Ivaldi,et al.  Brain–machine interfaces: computational demands and clinical needs meet basic neuroscience , 2003, Trends in Neurosciences.