Classification of motor imagery EEG signals based on STFTs

被引：0
|
作者：
Mu, Zhendong [1 ]
Xiao, Dan [1 ]
Hu, Jianfeng [1 ]
机构：
[1] Jiangxi Bluesky Univ, Inst Informat & Technol, Nanchang 330098, Jiangxi, Peoples R China
来源：
PROCEEDINGS OF THE 2009 2ND INTERNATIONAL CONGRESS ON IMAGE AND SIGNAL PROCESSING, VOLS 1-9 | 2009年
关键词：
Brain computer interface (BCI); time-frequency analysis; Fisher distance; EEG(electroencephalogram); COMPUTER INTERFACE BCI; NEURAL-NETWORKS; WAVELET TRANSFORM; BRAIN POTENTIALS; TIME; RECOGNITION;
D O I：
暂无
中图分类号：
TP18 [人工智能理论];
学科分类号：
081104 ; 0812 ; 0835 ; 1405 ;
摘要：
Human motor imagery tasks evoke electroencephalogram (EEG) signal changes. We describe a new technique for the classification of motor imagery electroencephalogram (EEG) recordings. The technique is based on a time-frequency analysis of EEG signals, regarding the relations between the EEG data obtained from the C3/C4 electrodes, the features were reduced according the Fisher distance. This reduced feature set is finally fed to a linear discriminant for classification. The algorithm was applied to 3 subjects, the classification performance of the proposed algorithm varied between 70% and 93.1%; across subjects.
引用
收藏
页码：181 / 184
页数：4
相关论文
共 50 条
  • [1] Classification of motor imagery EEG signals based on energy entropy
    Xiao, Dan
    Mu, Zhengdong
    Hu, Jianfeng
    [J]. 2009 INTERNATIONAL SYMPOSIUM ON INTELLIGENT UBIQUITOUS COMPUTING AND EDUCATION, 2009, : 61 - 64
  • [2] Classification of Motor Imagery EEG Signals Based on Channel Attention Mechanism
    Yu, Yue
    Ji, Wenkai
    Zhao, Liming
    Sun, Zhongbo
    Liu, Keping
    [J]. 2023 IEEE 12TH DATA DRIVEN CONTROL AND LEARNING SYSTEMS CONFERENCE, DDCLS, 2023, : 1720 - 1725
  • [3] Preliminary study of pedaling motor imagery classification based on EEG signals
    Rodriguez-Ugarte, M.
    Angulo-Sherman, I. N.
    Ianez, E.
    Ortiz, M.
    Azorin, J. M.
    [J]. 2017 INTERNATIONAL SYMPOSIUM ON WEARABLE ROBOTICS AND REHABILITATION (WEROB), 2017, : 72 - 73
  • [4] EEG Signals Based Motor Imagery and Movement Classification for BCI Applications
    Tasar, Beyda
    Yaman, Orhan
    [J]. 2022 INTERNATIONAL CONFERENCE ON DECISION AID SCIENCES AND APPLICATIONS (DASA), 2022, : 1425 - 1429
  • [5] Classification of Motor Imagery Based EEG Signals Using Sparsity Approach
    Sreeja, S. R.
    Rabha, Joytirmoy
    Samanta, Debasis
    Mitra, Pabitra
    Sarma, Monalisa
    [J]. INTELLIGENT HUMAN COMPUTER INTERACTION, IHCI 2017, 2017, 10688 : 47 - 59
  • [6] Simultaneous classification of motor imagery and SSVEP EEG signals
    Dehzangi, Omid
    Zou, Yuan
    Jafari, Roozbeh
    [J]. 2013 6TH INTERNATIONAL IEEE/EMBS CONFERENCE ON NEURAL ENGINEERING (NER), 2013, : 1303 - 1306
  • [7] Classification of Motor Imagery EEG Signals Based on Wavelet Transform and Sample Entropy
    Ma, Manzhen
    Guo, Libin
    Su, Kuifeng
    Liang, Deqian
    [J]. 2017 IEEE 2ND ADVANCED INFORMATION TECHNOLOGY, ELECTRONIC AND AUTOMATION CONTROL CONFERENCE (IAEAC), 2017, : 905 - 910
  • [8] ASTERI: image-based representation of EEG signals for motor imagery classification
    Gomes J.C.
    Rodrigues M.C.A.
    dos Santos W.P.
    [J]. Research on Biomedical Engineering, 2022, 38 (2) : 661 - 681
  • [9] Classification of EEG Motor imagery multi class signals based on Cross Correlation
    Krishna, D. Hari
    Pasha, I. A.
    Savithri, T. Satya
    [J]. INTERNATIONAL CONFERENCE ON COMPUTATIONAL MODELLING AND SECURITY (CMS 2016), 2016, 85 : 490 - 495
  • [10] Weighted sparse representation for classification of motor imagery EEG signals
    Sreeja, S. R.
    Himanshu
    Samanta, Debasis
    Sarma, Monalisa
    [J]. 2019 41ST ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY (EMBC), 2019, : 6180 - 6183
12345