Download PDF
Methods Inf Med 1997; 36(04/05): 298-301
DOI: 10.1055/s-0038-1636881
Original Article
Schattauer GmbH

Visualization of EEG Using Time-Frequency Distributions

B. Zhang Stiber
1   Department of Computer Science, The Hong Kong University of Science and Technology, Hong Kong, Japan
,
S. Sato
2   Department of Biophysical Engineering, Faculty of Engineering Science, Osaka University, Osaka, Japan
› Author Affiliations
Further Information

Publication History

Publication Date:
19 February 2018 (online)

    Permissions and Reprints

Abstract:

The EEG is a time-varying or nonstationary signal. Frequency and amplitude are two of its significant characteristics, and are valuable clues to different states of brain activity. Detection of these temporal features is important in understanding EEGs. Commonly, spectrograms and AR models are used for EEG analysis. However, their accuracy is limited by their inherent assumption of stationarity and their trade-off between time and frequency resolution. We investigate EEG signal processing using existing compound kernel time-frequency distributions (TFDs). By providing a joint distribution of signal intensity at any frequency along time, TFDs preserve details of the temporal structure of the EEG waveform, and can extract its time-varying frequency and amplitude features. We expect that this will have significant implications for EEG analysis and medical diagnosis.

Keywords:

EEG Processing - Time-Varying Spectra - Time-Frequency Distribution
 

  • REFERENCES

  • 1 Jansen B, Bourne J, Ward J. Autoregressive estimation of short segment spectra for computerized EEG analysis. IEEE Trans Biomed Eng 1981; 28: 630-8.
    PubMedGoogle Scholar
  • 2 Sharma A, Roy R. Analysis of hidden nodes in a neural network trained for pattern classification of EEG data. Proc 15th Ann Int’l Conf IEEE-EMBS 1993; 252-3.
    PubMedGoogle Scholar
  • 3 Veselis R, Reinsei R, Alagesan R, Hemo R, Bedford R. The EEG as a monitor of midazalom amnesia changes in power and topography as a function of amnestic state. Anesthesiology 1991; 74: 866-74.
    CrossrefPubMedGoogle Scholar
  • 4 Blackman R, Tukey J. The measurement of power spectra from the point of view of communications engineering-part i,ii. Bell Syst Tech J 1958; 37: 185-284 485-569.
    CrossrefPubMedGoogle Scholar
  • 5 Zhang B, Sato S. A time-frequency distribution of Cohen’s class with a compound kernel and its application to speech signal processing. IEEE Trans Signal Proc 1994; 42: 54-64.
    CrossrefPubMedGoogle Scholar
  • 6 Zhang B, Sato S, Ching P. Speech signal processing using a time-frequency distribution of Cohen’s class. In: IEEE-SP International Symposium on Time-Frequency and TimeScale Analysis. Philadelphia: IEEE; 1994: 624-7.
    Google Scholar
  • 7 Akay M. Detection and Estimation Methods for Biomedical Signals. . San Diego: Academic Press; 1996
    Google Scholar