The Study of Object-Oriented Motor Imagery Based on EEG Suppression

被引:26
|
作者
Li, Lili [1 ]
Wang, Jing [1 ]
Xu, Guanghua [1 ,2 ]
Li, Min [1 ]
Xie, Jun [1 ]
机构
[1] Xi An Jiao Tong Univ, Sch Mech Engn, Xian, Shaanxi, Peoples R China
[2] Xi An Jiao Tong Univ, State Key Lab Mfg Syst Engn, Xian, Shaanxi, Peoples R China
来源
PLOS ONE | 2015年 / 10卷 / 12期
基金
中国国家自然科学基金;
关键词
GRANGER CAUSALITY; EFFECTIVE CONNECTIVITY; PHYSICAL PRACTICE; FUNCTIONAL MRI; MU-RHYTHM; PERFORMANCE; MOVEMENT; AREAS; SYNCHRONIZATION; TOOLBOX;
D O I
10.1371/journal.pone.0144256
中图分类号
O [数理科学和化学]; P [天文学、地球科学]; Q [生物科学]; N [自然科学总论];
学科分类号
07 ; 0710 ; 09 ;
摘要
Motor imagery is a conventional method for brain computer interface and motor learning. To avoid the great individual difference of the motor imagery ability, object-oriented motor imagery was applied, and the effects were studied. Kinesthetic motor imagery and visual observation were administered to 15 healthy volunteers. The EEG during cue-based simple imagery (SI), object-oriented motor imagery (OI), non-object-oriented motor imagery (NI) and visual observation (VO) was recorded. Study results showed that OI and NI presented significant contralateral suppression in mu rhythm (p < 0.05). Besides, OI exhibited significant contralateral suppression in beta rhythm (p < 0.05). While no significant mu or beta contralateral suppression could be found during VO or SI (p > 0.05). Compared with NI, OI showed significant difference (p < 0.05) in mu rhythm and weak significant difference (p = 0.0612) in beta rhythm over the contralateral hemisphere. The ability of motor imagery can be reflected by the suppression degree of mu and beta frequencies which are the motor related rhythms. Thus, greater enhancement of activation in mirror neuron system is involved in response to object-oriented motor imagery. The object-oriented motor imagery is favorable for improvement of motor imagery ability.
引用
收藏
页数:10
相关论文
共 50 条
  • [1] Object-oriented crop classification based on UAV remote sensing imagery
    ZHANG Lan
    ZHANG Yanhong
    Global Geology, 2022, 25 (01) : 60 - 68
  • [2] An object-oriented classification method of high resolution imagery based on improved AdaTree
    Zhang Xiaohe
    Zhai Liang
    Zhang Jixian
    Sang Huiyong
    35TH INTERNATIONAL SYMPOSIUM ON REMOTE SENSING OF ENVIRONMENT (ISRSE35), 2014, 17
  • [3] Mapping China's mangroves based on an object-oriented classification of Landsat imagery
    Jia, Mingming
    Wang, Zongming
    Li, Lin
    Song, Kaishan
    Ren, Chunying
    Liu, Bo
    Mao, Dehua
    WETLANDS, 2014, 34 (02) : 277 - 283
  • [4] Mapping China’s mangroves based on an object-oriented classification of Landsat imagery
    Mingming Jia
    Zongming Wang
    Lin Li
    Kaishan Song
    Chunying Ren
    Bo Liu
    Dehua Mao
    Wetlands, 2014, 34 : 277 - 283
  • [5] OBJECT-ORIENTED PROGRAMMING WITHOUT AN OBJECT-ORIENTED LANGUAGE
    BOOCH, G
    SEIDEWITZ, E
    START, M
    FIRESMITH, D
    SIGPLAN NOTICES, 1986, 21 (11): : 508 - 508
  • [6] Object-Oriented Modeling of Object-Oriented Concepts A Case Study in Structuring an Educational Domain
    Pedroni, Michela
    Meyer, Bertrand
    TEACHING FUNDAMENTAL CONCEPTS OF INFORMATICS, PROCEEDINGS, 2010, 5941 : 155 - 169
  • [7] Object-Oriented Road Detection from Google Earth Imagery
    Tong, Biao
    PROCEEDINGS OF 2010 INTERNATIONAL SYMPOSIUM ON IMAGE ANALYSIS AND SIGNAL PROCESSING, 2010, : 413 - 416
  • [8] Review of Remotely Sensed Imagery Classification Patterns Based on Object-oriented Image Analysis
    LIU Yongxue1
    2. Department of Geography
    Chinese Geographical Science, 2006, (03) : 282 - 288
  • [9] Graph-Based Feature Selection for Object-Oriented Classification in VHR Airborne Imagery
    Chen, Xi
    Fang, Tao
    Huo, Hong
    Li, Deren
    IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING, 2011, 49 (01): : 353 - 365
  • [10] Review of Remotely Sensed Imagery Classification Patterns Based on Object-oriented Image Analysis
    Liu Yongxue
    Li Manchun
    Mao Liang
    Xu Feifei
    Huang Shuo
    CHINESE GEOGRAPHICAL SCIENCE, 2006, 16 (03) : 282 - 288