Multi-resolution time-frequency analysis for detection of rhythms of EEG signals

被引:0
|
作者
Qin, SR [1 ]
Ji, Z [1 ]
机构
[1] Chongqing Univ, Test Ctr, Chongqing 400030, Peoples R China
关键词
EEG signals; basic rhythms; STFT; wavelet transform; multi-resolution time-frequency analysis;
D O I
暂无
中图分类号
TP31 [计算机软件];
学科分类号
081202 ; 0835 ;
摘要
In recent years, various time-frequency methods have been applied widely For detecting all kinds of feature waves and abnormal waves in EEG signals. But because of the nature and some limitation of themselves, their application in EEG analysis has been limited. Considering the excellence and shortcoming of STFT and wavelet, in the "virtual EEG recording and analysis instrumentation", the multi-resolution time-frequency analysis method based on STFT and wavelet packet transform has been introduced to advance the self-adaptive ability for signals, so more flexible division of frequency bands in EEG can be obtained and the basic rhythms in EEG signals can be detected efficiently.
引用
收藏
页码:338 / 341
页数:4
相关论文
共 50 条
  • [31] Specific Movement Detection In EEG Signal Using Time-Frequency Analysis
    Piroska, Haller
    Janos, Szalai
    [J]. COMPLEXITY IN ARTIFICIAL AND NATURAL SYSTEMS, PROCEEDINGS, 2008, : 230 - 236
  • [32] Time-frequency analysis of biomedical signals
    Bianchi, AM
    Mainardi, LT
    Cerutti, S
    [J]. TRANSACTIONS OF THE INSTITUTE OF MEASUREMENT AND CONTROL, 2000, 22 (03) : 215 - 230
  • [33] Time-frequency analysis of musical signals
    Pielemeier, WJ
    Wakefield, GH
    Simoni, MH
    [J]. PROCEEDINGS OF THE IEEE, 1996, 84 (09) : 1216 - 1230
  • [34] Specific movement detection in EEG signal using time-frequency analysis
    Piroska, Haller
    Janos, Szalai
    [J]. 2008 COMPLEXITY & INTELLIGENCE OF THE ARTIFICIAL & NATURAL COMPLEX SYSTEMS, MEDICAL APPLICATIONS OF THE COMPLEX SYSTEMS, BIOMEDICAL COMPUTING, 2008, : 209 - 215
  • [35] High resolution time-frequency analysis method for extracting the epileptiform EEG patterns
    Liu, Jianping
    Tao, Weizhong
    Zheng, Chongxun
    Huang, Yuangui
    [J]. Hsi-An Chiao Tung Ta Hsueh/Journal of Xi'an Jiaotong University, 1997, 31 (09): : 97 - 101
  • [36] Time-frequency distributions in the classification of epilepsy from EEG signals
    Musselman, Marcus
    Djurdjanovic, Dragan
    [J]. EXPERT SYSTEMS WITH APPLICATIONS, 2012, 39 (13) : 11413 - 11422
  • [37] Characterization of Daytime Sleepiness by Time-Frequency Measures of EEG Signals
    Melia, Umberto
    Guaita, Marc
    Vallverdu, Montserrat
    Claria, Francesc
    Montserrat, Josep M.
    Vilaseca, Isabel
    Salamero, Manel
    Gaig, Carles
    Caminal, Pere
    Santamaria, Joan
    [J]. JOURNAL OF MEDICAL AND BIOLOGICAL ENGINEERING, 2015, 35 (03) : 406 - 417
  • [38] Time-frequency evaluation of segmentation methods for neonatal EEG signals
    Wong, Lisa
    Abdulla, Waleed
    [J]. 2006 28TH ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY, VOLS 1-15, 2006, : 865 - 868
  • [39] Parameterized time-frequency analysis to separate multi-radar signals
    Wenlong Lu
    Junwei Xie
    Heming Wang
    Chuan Sheng
    [J]. Journal of Systems Engineering and Electronics, 2017, 28 (03) : 493 - 502
  • [40] CLASSIFICATION OF EEG SIGNALS USING ADAPTIVE TIME-FREQUENCY DISTRIBUTIONS
    Khan, Nabeel A.
    Ali, Sadiq
    [J]. METROLOGY AND MEASUREMENT SYSTEMS, 2016, 23 (02) : 251 - 260