Recognition of motor imagery EEG patterns based on common feature analysis

被引：6
|
作者：
Huang, Zhenhao [1 ,2 ]
Qiu, Yichun [1 ,3 ]
Sun, Weijun [1 ,4 ]
机构：
[1] Guangdong Univ Technol, Sch Automat, Guangzhou, Peoples R China
[2] Guangdong Hong Kong Macao Joint Lab Smart Mfg, Guangzhou, Peoples R China
[3] Minist Educ, Key Lab Intelligent Detect & Internet Things Mfg, Guangzhou, Peoples R China
[4] Guangdong Key Lab IoT Informat Technol, Guangzhou, Peoples R China
来源：
BRAIN-COMPUTER INTERFACES | 2021年 / 8卷 / 04期
基金：
中国国家自然科学基金;
关键词：
Brain-computer interface; motor imagery; common feature analysis; tensor decomposition; BRAIN-COMPUTER INTERFACES; CLASSIFICATION; DISCRIMINATION; COMMUNICATION; SIGNALS; FILTERS;
D O I：
10.1080/2326263X.2020.1783170
中图分类号：
R318 [生物医学工程];
学科分类号：
0831 ;
摘要：
Motor imagery (MI) is particularly attractive in brain-computer interface (BCI) in the sense that it does not need any external stimuli. However, the overall performance is often severely affected by subject's mental states. In this study, a method based on common feature analysis (CFA) was proposed for MI electroencephalogram (EEG) patterns recognition, which can not only improve the recognition accuracy but also help to find reliable and interpretable features associated with specific MI patterns. Evaluation using several open competition datasets justifies that the common features could more accurately identify MI characteristics and hence substantially benefit MI EEG patterns recognition.
引用
下载
收藏
页码：128 / 136
页数：9
相关论文
共 50 条
  • [1] Feature Weighting and Regularization of Common Spatial Patterns in EEG-Based Motor Imagery BCI
    Mishuhina, Vasilisa
    Jiang, Xudong
    IEEE SIGNAL PROCESSING LETTERS, 2018, 25 (06) : 783 - 787
  • [2] Spectral feature extraction from EEG based motor imagery using common spatial patterns
    Moufassih, Mustapha
    Tarahi, Oussama
    Hamou, Soukaina
    Agounad, Said
    Azami, Hafida Idrissi
    2022 2ND INTERNATIONAL CONFERENCE ON INNOVATIVE RESEARCH IN APPLIED SCIENCE, ENGINEERING AND TECHNOLOGY (IRASET'2022), 2022, : 1017 - 1022
  • [3] EEG Feature Extraction for Motor Imagery Task Based on Bispectrum Analysis
    Qiao Xiaoyan
    Xu Chunyuan
    2011 INTERNATIONAL CONFERENCE ON INTELLIGENT COMPUTATION AND INDUSTRIAL APPLICATION (ICIA2011), VOL I, 2011, : 43 - 46
  • [4] EEG Feature Extraction for Motor Imagery Task Based on Bispectrum Analysis
    Qiao Xiaoyan
    Xu Chunyuan
    2010 THE 3RD INTERNATIONAL CONFERENCE ON COMPUTATIONAL INTELLIGENCE AND INDUSTRIAL APPLICATION (PACIIA2010), VOL VI, 2010, : 43 - 46
  • [5] Feature Combination and Correlation Analysis for Motor Imagery EEG
    Li, Mingai
    Xu, Jinfeng
    Luo, Xinyong
    PROCEEDINGS OF THE 2016 INTERNATIONAL FORUM ON MANAGEMENT, EDUCATION AND INFORMATION TECHNOLOGY APPLICATION, 2016, 47 : 717 - 721
  • [6] Impact of Feature Selection on EEG Based Motor Imagery
    Sahu, Mridu
    Shukla, Sneha
    INFORMATION AND COMMUNICATION TECHNOLOGY FOR COMPETITIVE STRATEGIES, 2019, 40 : 749 - 762
  • [7] EEG-based Motor Imagery Feature Extraction
    Liu, Yang
    Li, Niandiang
    Li, Yongxiang
    ADVANCES IN MECHATRONICS, AUTOMATION AND APPLIED INFORMATION TECHNOLOGIES, PTS 1 AND 2, 2014, 846-847 : 944 - 947
  • [8] Analysis and intention recognition of motor imagery EEG signals based on multi-feature convolutional neural network
    He Q.
    Shao D.
    Wang Y.
    Zhang Y.
    Xie P.
    Yi Qi Yi Biao Xue Bao/Chinese Journal of Scientific Instrument, 2020, 41 (01): : 138 - 146
  • [9] Motor Imagery EEG Recognition Based on Biomimetic Pattern Recognition
    Xu, Kai
    Wu, Yan
    2010 3RD INTERNATIONAL CONFERENCE ON BIOMEDICAL ENGINEERING AND INFORMATICS (BMEI 2010), VOLS 1-7, 2010, : 955 - 959
  • [10] Recognition of motor imagery EEG based on AR and SVM
    Zhang, Yi
    Yang, Liu
    Li, Min
    Luo, Yuan
    Huazhong Keji Daxue Xuebao (Ziran Kexue Ban)/Journal of Huazhong University of Science and Technology (Natural Science Edition), 2011, 39 (SUPPL. 2): : 103 - 106
12345