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猕猴手指移动神经解码线性时不变模型的时间相关性研究

冯景义 吴海锋 曾玉

冯景义, 吴海锋, 曾玉.猕猴手指移动神经解码线性时不变模型的时间相关性研究.自动化学报, 2021, 47(2): 442-452 doi: 10.16383/j.aas.c180098
引用本文: 冯景义, 吴海锋, 曾玉.猕猴手指移动神经解码线性时不变模型的时间相关性研究.自动化学报, 2021, 47(2): 442-452 doi: 10.16383/j.aas.c180098
Feng Jing-Yi, Wu Hai-Feng, Zeng Yu. Time correlation of time-invariant linear models in neural decoding for the macaque's moving flnger. Acta Automatica Sinica, 2021, 47(2): 442-452 doi: 10.16383/j.aas.c180098
Citation: Feng Jing-Yi, Wu Hai-Feng, Zeng Yu. Time correlation of time-invariant linear models in neural decoding for the macaque's moving flnger. Acta Automatica Sinica, 2021, 47(2): 442-452 doi: 10.16383/j.aas.c180098

猕猴手指移动神经解码线性时不变模型的时间相关性研究

doi: 10.16383/j.aas.c180098
基金项目: 

国家自然科学基金 61762093

云南省科技厅第十七批省中青年学术和技术带头人 2014HB019

云南省重点应用和基础研究基金 2018FA036

云南省教育厅科学研究基金项目 2018Y106

云南民族大学研究生创新基金科研项目 2018YJCXS175

详细信息
    作者简介:

    冯景义  云南民族大学电气信息工程学院硕士研究生.主要研究方向为神经网络和机器学习. E-mail:fengjingyione@foxmail.com

    曾玉   云南民族大学助理教授.主要研究方向为无线网络控制和移动通信. E-mail: yv.zeng@gmail.com

    通讯作者:

    吴海锋   云南民族大学教授.主要研究方向为机器学习, 移动通信和神经信号处理.本文通信作者. E-mail: whf5469@gmail.com

  • 本文责任编委 秦涛

Time Correlation of Time-invariant Linear Models in Neural Decoding for the Macaque's Moving Finger

Funds: 

National Natural Science Foundation of China 61762093

The 17th batches of Young and Middleaged Leaders in Academic and Technical Reserved Talents Project of Yunnan Province 2014HB019

The Key Applied and Basic Research Foundation of Yunnan Province 2018FA036

The Science Research Fund Program in Education Department of Yunnan Province 2018Y106

The Graduate Student Innovation Fund Research Project in Yunnan Minzu University 2018YJCXS175

More Information
    Author Bio:

    FENG Jing-Yi  Master student at the School of Electrical and Information Engineering, Yunnan Minzu University. His research interest covers neural network and machine learning

    ZENG Yu  Assistant professor at Yunnan Minzu University. Her research interest covers wireless network control, mobile communications

    Corresponding author: WU Hai-Feng  Porfessor at Yunnan Minzu University. His research interest covers machine learning, mobile communications and neural signal processing. Corresponding author of this paper
  • Recommended by Associate Editor QIN Tao
  • 摘要: 利用猕猴运动皮层神经元峰电位数信号估计其手指移动位置是一神经解码问题, 通常采用时不变线性模型(Time-invariant linear model, TILM)来解决.本文分析了传统TILM模型的时间相关性问题, 依据猕猴手指移动位置的连续性特点, 采用一种新的模型去解码其手指移动位置, 称之为卷积空间模型(Convolution space model, CSM).与传统的模型相比, 卷积空间模型不但将当前时刻的状态与前一个时刻建立了相关, 而且与前多个时刻的状态也有相关.在实验中, 利用公开数据来评判本文方法的解码性能, 实验结果表明, 传统方法的解码误差要大于CSM模型的方法, 因此CSM模型具有更好的解码准确性.
    Recommended by Associate Editor QIN Tao
    1)  本文责任编委 秦涛
  • 图  1  猕猴手指移动轨迹编码

    Fig.  1  Macaque finger movement track coding

    图  2  二维卷积空间模型示意图

    Fig.  2  Two dimensional convolution space model

    图  3  时间相关性下卷积核权重大小分布

    Fig.  3  Convolution kernel weight distribution in time correlation

    图  4  实验1中位置估计与手指移动真实位置曲线

    Fig.  4  Position estimation and finger movement real position curve in experiment 1

    图  5  手指移动横坐标估计误差随延迟数据长度$P$的变化

    Fig.  5  Finger movement abscissa estimation error with delay data length $P$ changes

    图  6  手指移动横坐标估计误差随迭代次数$T$的变化

    Fig.  6  Finger movement abscissa estimation error with the number of iterations cycle $T$ changes

    表  1  参数$P$对算法的训练复杂度

    Table  1  Training complexity of parameter $P$ for algorithm

    CSM模型算法LSRLSGDA
    复杂度${\rm O}(P^3)$${\rm O}(TP^3)$${\rm O}(TP^2)$
    下载: 导出CSV

    表  2  $X$轴和$Y$轴(括号内)的估计误差(cm) (保留三位)

    Table  2  $X$-axis and $Y$-axis (in parentheses) estimated error (cm) (three places reserved)

    算法$X(Y)$实验2实验3实验4实验5平均
    Linear3.813 (2.003)3.941 (2.513)4.135 (2.991)3.919 (2.120)3.952 (2.407)
    KF3.060 (1.498)3.908 (2.042)4.540 (2.939)3.637 (1.656)3.786 (2.034)
    RBE3.637 (1.727)4.699 (2.295)3.907 (2.400)3.913 (1.936)4.015 (2.089)
    UCKD6.596 (4.235)6.949 (5.504)7.500 (5.775)6.220 (4.868)6.816 (5.096)
    CSM-LS2.959 (1.411)3.450 (2.154)3.937 (2.921)3.109 (1.724)3.364 (2.052)
    CSM-RLS2.964 (1.411)3.443 (2.153)3.957 (2.891)3.106 (1.726)3.368 (2.045)
    CSM-GDA2.896 (1.500)3.270 (2.095)4.581 (2.406)3.233 (1.709)3.495 (1.927)
    下载: 导出CSV

    表  3  二维平面的估计误差(cm) (保留三位)

    Table  3  The estimated error of the two-dimensional plane (cm) (three places reserved)

    算法$XY$实验2实验3实验4实验5平均
    Linear4.3074.6745.1034.4554.635
    KF3.4074.4095.4083.9964.305
    RBE4.0265.2304.5854.3664.552
    UCKD7.8398.8659.4667.8968.787
    CSM-LS3.2784.0684.9023.5553.951
    CSM-RLS3.2834.0614.9013.5533.950
    CSM-GDA3.2613.8835.1743.6563.994
    下载: 导出CSV

    表  4  CSM模型算法的训练时间(保留三位)

    Table  4  The training time of CSM model algorithm (three places reserved)

    CSM模型算法LSRLSGDA
    训练时间(s)1.06123.8531.285
    下载: 导出CSV
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出版历程
  • 收稿日期:  2018-02-12
  • 录用日期:  2018-08-28
  • 刊出日期:  2021-02-26

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