Please wait a minute...
浙江大学学报(工学版)
浙江大学学报(工学版)  2021, Vol. 55 Issue (12): 2315-2322    DOI: 10.3785/j.issn.1008-973X.2021.12.011
机械工程     
基于样本熵与时频分析的手部抓握运动意图预测
许重宝(),张虹淼*(),孙立宁,李娟,樊炎,郭浩
苏州大学 江苏省先进机器人技术重点实验室, 江苏 苏州 215021
Prediction of hand grasping intention based on sample entropy and time-frequency analysis
Chong-bao XU(),Hong-miao ZHANG*(),Li-ning SUN,Juan LI,Yan FAN,Hao GUO
Jiangsu Provincial Key Laboratory of Advanced Robotics, Soochow University, Suzhou 215021, China
 全文: PDF(1496 KB)   HTML
摘要：

针对上肢外骨骼控制信号产生与外部设备响应存在时间滞后导致脑机接口(BCI)系统实时性差的问题，采集被试手部自主抓握前运动相关皮质电位(MRCP) ，提出基于非线性复杂度特征样本熵 (SampEn)与线性幅值特征融合算法的手部运动意图预测方法. 从时频、神经复杂度分析不同大脑状态之间存在的差异，通过特征融合实现对手部抓握运动意图的预测. 基于特征融合意图离线预测准确率最高可达88.46%，可以在人体手部自主运动发生时刻?1 400 ms实现对手部运动预测. 与平静时期手部静止状态相比，被试产生手部抓握运动意图时脑电信号的功率谱与复杂度均产生明显变化，为基于手部运动意图预测提前驱动机器人实现人机协同提供控制策略.
关键词： 运动意图;  运动相关皮质电位;  非线性分析;  复杂度    
Abstract:

The movement related cortical potentials (MRCP) before the autonomous grasping movement of the hand were collected, and based on the fusion algorithm of nonlinear sample entropy (SampEn) feature and linear amplitude feature a prediction method of motion intention was proposed, in order to solve the problem of poor real-time of brain computer interface (BCI) system caused by the time lag between the control signal of upper limb exoskeleton and the response of external devices. The differences between different brain states were analyzed from the perspectives of time-frequency and neural complexity, and the prediction of hand grasping motor intention by feature fusion was realized. The off-line classification accuracy of intention based on the feature fusion was up to 88.46%, when the occurrence time of human hand voluntary motion was ?1400 ms the prediction of hand motion can be realized. The EEG signals of hand grasping intention in the power spectrum and complexity are significantly different from those in the static state of the hand during the quiet period which could be proposed as a control strategy based on hand motion intention to drive robot in advance to realize human-computer cooperation.
Key words: motion intention    movement-related cortical potential    nonlinear analysis    complexity
收稿日期: 2020-01-07 出版日期: 2021-12-31
CLC:  TP 249  
基金资助: 国家自然科学基金资助项目(U1713218)；广东省重点领域研发计划项目(2020B010165004)
通讯作者: 张虹淼     E-mail: 20185229042@stu.suda.edu.cn;zhanghongmiao@suda.edu.cn
作者简介: 许重宝(1995?)，男，硕士生. 从事脑机接口研究. orcid.org/0000-0002-3707-1609. E-mail： 20185229042@stu.suda.edu.cn
服务　　
把本文推荐给朋友
加入引用管理器
E-mail Alert
作者相关文章　　
许重宝
张虹淼
孙立宁
李娟
樊炎
郭浩

引用本文:

许重宝,张虹淼,孙立宁,李娟,樊炎,郭浩. 基于样本熵与时频分析的手部抓握运动意图预测[J]. 浙江大学学报(工学版), 2021, 55(12): 2315-2322.

Chong-bao XU,Hong-miao ZHANG,Li-ning SUN,Juan LI,Yan FAN,Hao GUO. Prediction of hand grasping intention based on sample entropy and time-frequency analysis. Journal of ZheJiang University (Engineering Science), 2021, 55(12): 2315-2322.

链接本文:

https://www.zjujournals.com/eng/CN/10.3785/j.issn.1008-973X.2021.12.011    或    https://www.zjujournals.com/eng/CN/Y2021/V55/I12/2315

图 1  运动相关皮质电位采集实验过程及设备
图 2  手部抓握运动脑肌电信号采集处理流程图
图 3  手部抓握运动脑地形图及通道位置
图 4  样本熵算法流程图
图 5  手部抓握运动C1通道时频分析结果
图 6  手部抓握实验的嵌入维数与时间延迟
图 7  不同大脑状态EEG信号在m=2, τ=5的相空间轨迹
图 8  手部抓握运动时不同频带EEG信号复杂度时间过程
图 9  不同被试手部抓握运动意图预测准确率结果
算法 p/% FP/(FPS·min?1)
Ou等[24] 75±10 -
Jochumsen等[25] 86±10 2.5±0.9
Zeng等[26] 88.05±8.80 0.57±0.68
Liu等[27] 81±5 <8
本文 80.75±3.28 0.22±0.11
表 1  国内外运动意图预测研究离线分析比较结果
被试 p/ % t/ms
LDA SVM LDA SVM
Ⅰ 83.33 76.92 ?2 000 ?2 000
Ⅱ 84.62 88.89 ?1 208 ?1 041
Ⅲ 86.67 81.67 ?1 363 ?1 681
Ⅳ 76.67 73.33 ?1 307 ?1 307
Ⅴ 80.95 76.19 ?1 400 ?1 650
表 2  伪在线实验机制预测时间结果
1 ROBINSON N, CHESTAR W J, SMITHA K G Use of mobile EEG in decoding hand movement speed and position[J]. IEEE Transactions on Human-Machine Systems, 2021, 51 (2): 120- 129
doi: 10.1109/THMS.2021.3056274
2 KO W, JEON E, JEONG S, et al Multi-scale neural network for EEG representation learning in BCI[J]. IEEE Computational Intelligence Magazine, 2021, 16 (2): 31- 45
doi: 10.1109/MCI.2021.3061875
3 GAO H B, LUO L, PI M, et al EEG-based volitional control of prosthetic legs for walking in different terrains[J]. IEEE Transactions on Automation Science and Engineering, 2021, 18 (2): 530- 540
doi: 10.1109/TASE.2019.2956110
4 LEIGH H, DANIEL B, BEATA J, et al Reach and grasp by people with tetraplegia using a neurally controlled robotic arm[J]. Nature, 2012, 485 (7398): 372- 375
doi: 10.1038/nature11076
5 CRUZ A, PIRES G, LOPES A, et al A self-paced BCI with a collaborative controller for highly reliable wheelchair driving: experimental tests with physically disabled individuals[J]. IEEE Transactions on Human-Machine Systems, 2021, 51 (2): 109- 119
doi: 10.1109/THMS.2020.3047597
6 LIN J J, LIANG L Y, HAN X, et al Cross-target transfer algorithm based on the volterra model of SSVEP-BCI[J]. Tsinghua Science and Technology, 2021, 26 (4): 505- 522
doi: 10.26599/TST.2020.9010015
7 NAJJAR A B, ALSAHLY N, ALSHAMASS R, et al Toward the optimization of the region-based P300 speller[J]. Computers, Materials and Continua, 2021, 67 (1): 1169- 1189
doi: 10.32604/cmc.2021.014140
8 FENG Z Q, HE Q H, ZHANG J N, et al A hybrid BCI system based on motor imagery and transient visual evoked potential[J]. Multimedia Tools and Applications, 2020, 79 (15): 10327- 10340
doi: 10.1007/s11042-019-7607-3
9 李瀚哲, 张小栋, 李睿, 等 利用运动准备电位的人体下肢自主运动意图预先感知方法[J]. 西安交通大学学报, 2019, 53 (10): 16- 23
LI Han-zhe, ZHANG Xiao-dong, LI Rui, et al A preperception method for voluntary movement intention of lower limb using readiness potential[J]. Journal of Xi’an Jiaotong University, 2019, 53 (10): 16- 23
10 YU K, LIU C, NIU X D, et al Transcranial focused ultrasound neuromodulation of voluntary movement-related cortical activity in humans[J]. IEEE Transactions on Bio-Medical Engineering, 2021, 68 (3): 1923- 1931
11 MISHRA A, SHARMA S, KUMAR S, et al Effect of hand grip actions on object recognition process: a machine learning-based approach for improved motor rehabilitation[J]. Neural Computing and Applications, 2020, 33 (7): 2339- 2350
doi: 10.1007/s00521-020-05125-w
12 OJHA M K, MUKUL M K A novel approach based on EMD to improve the performance of SSVEP based BCI system[J]. Wireless Personal Communications, 2021, 118 (4): 2455- 2467
doi: 10.1007/s11277-021-08135-6
13 吕金虎. 混沌时间序列分析及其应用[M]. 武汉: 武汉大学出版社, 2005: 25-30.
14 胡瑜, 陈涛 基于C-C算法的混沌吸引子的相空间重构技术[J]. 电子测量与仪器学报, 2012, 26 (5): 425- 430
HU Yu, CHEN Tao Phase-space reconstruction technology of chaotic attractor based on C-C method[J]. Journal of Electronic Measurement and Instrument, 2012, 26 (5): 425- 430
15 PINCUS S M, GOLDBERGER A L Physiological time-series analysis: what does regularity quantify?[J]. The American Journal of physiology, 1994, 266 (4): H1643- H1656
doi: 10.1152/ajpheart.1994.266.4.H1643
16 LUBETZKY A V, HAREL D, LUBETZKY E On the effects of signal processing on sample entropy for postural control[J]. PloS ONE, 2018, 13 (3): 1- 15
17 RICHMAN J S, MOORMAN J R Physiological time-series analysis using approximate entropy and sample entropy[J]. American Journal of Physiology-Heart and Circulatory Physiology, 2000, 47 (6): H2039- H2049
18 CHEN S F, LUO Z Z, GAN H T An entropy fusion method for feature extraction of EEG[J]. Neural Computing and Applications, 2018, 29 (10): 857- 863
doi: 10.1007/s00521-016-2594-z
19 PFURTSCHELLER G, DA SILVA F H L Event-related EEG/MEG synchronization and desynchronization: basic principles[J]. Clinical Neurophysiology, 1999, 110 (11): 1842- 1857
doi: 10.1016/S1388-2457(99)00141-8
20 孙颖, 马江河, 张雪英 结合非线性全局特征和谱特征的脑电情感识别[J]. 计算机工程与应用, 2018, 54 (17): 116- 121
SUN Ying, MA Jian-he, ZHANG Xue-ying EEG emotion recognition based on nonlinear global features and spectral feature[J]. Computer Engineering and Applications, 2018, 54 (17): 116- 121
doi: 10.3778/j.issn.1002-8331.1803-0027
21 王卫星, 孙守迁, 李超, 等 基于卷积神经网络的脑电信号上肢运动意图识别[J]. 浙江大学学报: 工学版, 2017, 51 (7): 1381- 1389
WANG Shou-Xing, SUN Shou-qian, LI Chao, et al Recognition of upper limb motion intention of EEG signal based on convolutional neural network[J]. Journal of Zhejiang Unive-rsity: Engineering Science, 2017, 51 (7): 1381- 1389
22 EZZATDOOST K, HOJJATI H, AGHAJAN H Decoding olfactory stimuli in EEG data using nonlinear features: a pilot study[J]. Journal of Neuroscience Methods, 2020, (341): 108780
23 明东, 顾斌, 蒋晟龙, 等 运动相关皮质电位在运动康复领域的应用[J]. 纳米技术与精密工程, 2015, 13 (6): 425- 433
MING Dong, GU Bin, JIANG Sheng-long, et al Application of movement-related cortical potentials in the field of motor rehabilitation[J]. Nanotechnology and Precision Engineering, 2015, 13 (6): 425- 433
24 OU B, RATHI V, LIN P, et al Prediction of human voluntary movement before it occurs[J]. Clinical Neurophysiology, 2011, 122 (2): 364- 372
doi: 10.1016/j.clinph.2010.07.010
25 JOCHUMSEN M, NIAZI I K, TAYLOR D, et al Detecting and classifying movement-related cortical potentials associated with hand movements in healthy subjects and stroke patients from single-electrode, single-trial EEG[J]. Journal of Neural Engineering, 2015, 12 (5): 056013
doi: 10.1088/1741-2560/12/5/056013
26 ZENG H, XU B, LI H, et al. Investigation of phase features of movement related cortical potentials for upper-limb movement intention detection [C]// ICIRA 2017 Intelligent Robotics and Applications. [S. l.] : Springer, 2017: 350-358.
[1] 段有康,陈小刚,桂剑,马斌,李顺芬,宋志棠. 基于相位划分的下肢连续运动预测[J]. 浙江大学学报(工学版), 2021, 55(1): 89-95.
[2] 梅振宇, 章伟. 基于复杂性测度的泊位占有率序列动力学分析[J]. 浙江大学学报(工学版), 2018, 52(4): 727-734.
[3] 王卫星, 孙守迁, 李超, 唐智川. 基于卷积神经网络的脑电信号上肢运动意图识别[J]. 浙江大学学报(工学版), 2017, 51(7): 1381-1389.
[4] 徐兵, 苏琦, 张军辉, 陆振宇. 比例放大器驱动电路特性分析及控制器设计[J]. 浙江大学学报(工学版), 2017, 51(4): 800-806.
[5] 董石麟, 苗峰, 陈伟刚, 周观根, 滕起, 董晟昊. 新型六杆四面体柱面网壳的构形、静力和稳定性分析[J]. 浙江大学学报(工学版), 2017, 51(3): 508-513.
[6] 欧阳丹丹, 付波, 童根树. 矩形钢管截面延性等级和板件宽厚比相关关系[J]. 浙江大学学报(工学版), 2016, 50(2): 271-281.
[7] 李勰, 陈水福. 门式刚架轻钢结构抗风安全性分析[J]. J4, 2013, 47(12): 2141-2145.
[8] 牛辉, 汪劲丰, 张巍, 俞亚南, 吴光宇. 基于实体退化单元的高墩非线性稳定仿真分析[J]. J4, 2012, 46(6): 1082-1089.
[9] 关富玲, 钱利锋. 新型陆基充气球天线力学分析与测试[J]. J4, 2012, 46(2): 257-262.
[10] 曹晓阳, 潘赟, 严晓浪, 宦若虹. 低面积-时间复杂度的离散余弦变换脉动结构[J]. J4, 2011, 45(4): 656-659.
[11] 张坤, 李东庆, 李建宇, 童刚强. 青藏高等级公路通风管试验路基降温效果[J]. J4, 2010, 44(10): 1845-1850.
[12] 董占勋, 孙守迁, 吴群, 等. 心率变异性与驾驶疲劳相关性研究[J]. J4, 2010, 44(1): 46-50.
[13] 刘云海 林宇. 基于运动复杂度的码率控制算法研究[J]. , 2009, 43(4): 710-715+742.
[14] 杨红 骆文进 王志军. 侧向力模式对框架地震反应的控制能力[J]. J4, 2008, 42(9): 1526-1531.
[15] 刘漪 黄爱苹. OFDM系统载波频偏的低复杂度多步估计[J]. J4, 2007, 41(8): 1293-1297.