Research of Partial Least Squares Decoding Method for Motion Intent_Journal of Biomedical Engineering

Authors：

School of Electrical Engineering, Zhengzhou University, Zhengzhou 450001, China;

Corresponding author：

Keywords：

motion intent; sparse coding; feature extraction; partial least squares; support vector machine

DOI：

10.7507/1001-5515.20160104

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Due to the sparsity of brain encoding, the neural ensemble signals recorded by microelectrode arrays contain a lot of noise and redundant information, which could reduce the stability and precision of decoding of motion intent. To solve this problem, we proposed a decoding method based on partial least squares (PLS) feature extraction in our study. Firstly, we extracted the features of spike signals using the PLS, and then classified them with support vector machine (SVM) classifier, and decoded them for motion intent. In this study, we decoded neural ensemble signals based on plus-maze test. The results have shown that the proposed method had a better stability and higher decoding accuracy, due to the PLS combined with classification model which overcame the shortcoming of PLS regression that was easily affected by accumulated effect of noise. Meanwhile, the PLS method extracted fewer features with more useful information in comparison with common feature extraction method. The decoding accuracy of real data sets were 93.59%, 84.00% and 83.59%, respectively.

Citation： WANHong, YANGHui, LIUXinyu, SHANGZhigang. Research of Partial Least Squares Decoding Method for Motion Intent. Journal of Biomedical Engineering, 2016, 33(4): 626-630. doi: 10.7507/1001-5515.20160104 Copy

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1. WELBERG L. Brain-machine interfaces:Restoring movement in a paralysed hand[J]. Nat Rev Neurosci, 2012, 13(6):360-361.
2. ETHIER C, OBY E R, BAUMAN M J, et al. Restoration of grasp following paralysis through brain-controlled stimulation of muscles[J]. Nature, 2012, 485(7398):368-371.
3. BARNES T D, KUBOTA Y, HU Dan, et al. Activity of striatal neurons reflects dynamic encoding and recoding of procedural memories[J]. Nature, 2005, 437(7062):1158-1161.
4. ZHANG H J, DAI J H, ZHANG S M, et al. Neuronal ensemble decoding of rat's motor cortex by Kalman filter and optimal linear estimation[C]//International Congress on Image and Signal Processing. Tianjin:2009:1-5.
5. CHAPIN J K, MOXON K A, MARKOWITZ R S, et al. Real-time control of a robot arm using simultaneously recorded neurons in the motor cortex[J]. Nat Neurosci, 1999, 2(7):664-670.
6. DAI Jianhua, YU Yi, ZHANG Shaomin, et al. Novel neuronal ensembles encoding analysis method based on rough set theory[C]//ICME International Conference on Complex Medical Engineering, 2009. Tempe, AZ:2009:40-44.
7. KRISHNAN A, WILLIAMS L J, MCINTOSH A R, et al. Partial Least Squares (PLS) methods for neuroimaging:a tutorial and review[J]. Neuroimage, 2011, 56(2):455-475.
8. 刘天玲, 苏琪雅, 孙群, 等.基于NIR分析和模式识别技术的玉米种子识别系统[J].光谱学与光谱分析, 2012, 32(5):1209-1212.
9. LUO K X, ZHONG F, ZHAO L, et al. Evaluation of classification methods in conjunction with dimensionality reduction techniques in mass spectrometry data classification model[J]. Scientia Sinica Vitae, 2010, 40(6):544-550.
10. CHAO Z C, NAGASAKA Y, FUJⅡ N. Long-term asynchronous decoding of arm motion using electrocorticographic signals in monkeys[J]. Front Neuroeng, 2010, 3:3.
11. 王蕊.特征选择方法在基于LFP信号的神经解码中的应用[D].杭州:浙江大学, 2013.
12. 朱凡, 李悦, 蒋凯, 等.基于偏最小二乘的大鼠初级运动皮层解码[J].浙江大学学报(工学版), 2013, 47(5):901-905.
13. CHEN C, SHIN D, WATANABE H, et al. Prediction of hand trajectory from electrocorticography signals in primary motor cortex[J]. PLoS One, 2013, 8(12):e83534.
14. VEIT L, HARTMANN K, NIEDER A. Neuronal correlates of visual working memory in the corvid endbrain[J]. J Neurosci, 2014, 34(23):7778-7786.
15. LENGERSDORF D, PUSCH R, GVNTVRKVN O, et al. Neurons in the pigeon nidopallium caudolaterale signal the selection and execution of perceptual decisions[J]. Eur J Neurosci, 2014, 40(9):3316-3327.

Journal of Biomedical Engineering

Research of Partial Least Squares Decoding Method for Motion Intent

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