Neural Signal Analysis And Decoding Of Primary Motor Cortex In Rats

Brain-machine interface (BMI) provides a directly communication channel between the brain and man-made devices independent of peripheral nervous system. The main functions of a BMI system include neural signals recording, neural signals analysis and decoding to predict the movement parameters and convert them into controlling commands of man-made devices. Research presented in this study provides a set of methods and schemes on feature extraction, analysis and information decoding of local field potential (LFP) signal and spike signal, using rat as the experiment object.Firstly, as a important preprocessing procedure feature extraction of LFP and spike were studied. For LFP, Morlet wavelet transform was applied to extract the multi-channel and multi-band time-frequency domain features. For spike, spike-sorting system based on Spectral Clustering and Naive Bayesian Classification algorithms was built. Experiment results show this systerm has properties of high precision, noise robustness and high computational efficiency.After feature extraction of neural signals, qualitative and quantitative analysis of the features were researched secondly. For qualitative analysis, a manifold algorithm namely Isomap was used to reduce the dimension of the high dimensional neural signal and visualize the variety pattern of the neural signals in a 3-D space. The visualization results show that both types of neural signals have regular variety patterns during the experiment movement but the patterns of LFP and spike are different. For quantitative analysis, mutual informations between the two types of high dimensional neural signal features and the motion parameter were estimated to analyze the relativity between neural signals and the movement. Relativity analysis results show that both LFP and spike contain abundant information about the movement and combining LFP and spike can get more useful information than taking each of the two types of signal alone on condition of small number of channels.At last, neural information decoding system were studied in order to decode and reconstruct the movement related information. Two kinds of neural feature reduction strategies, including Boosting-based sub-feature space selection algorithm and partial least square (PLS)-based dimension reduction algorithm, were applied to enhance the real-time and generalization capability of the decoding system. For the decoding algorithms, partial least square regression linear algorithm and kernel partial least square (KPLS) regression non-linear algorithm were studied and compared. After studying and comparing the advantages and disadvantages of linear and non-linear decoding algorithms, a Two Stage Model was built to achieve both the high computational efficient advantage of linear algorithms and the high decoding accuracy advantage of non-linear algorithms. Furthermore, the decoding power of combining LFP and spike was also analyzed in this study. Decoding results indicate that combining these two types of neural signals can improve both decoding accuracy and stability of the decoding model.