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Methods Inf Med 2004; 43(01): 70-73
DOI: 10.1055/s-0038-1633838
Original Article
Schattauer GmbH

Time-Frequency Analysis of Brain Electrical Activity – Adaptive Approximations

K. J. Blinowska
1   Laboratory of Medical Physics, Warsaw University, Warsaw, Poland
,
P. J. Durka
1   Laboratory of Medical Physics, Warsaw University, Warsaw, Poland
,
J. Z ygierewicz
1   Laboratory of Medical Physics, Warsaw University, Warsaw, Poland
› Author Affiliations
Further Information

Publication History

Publication Date:
07 February 2018 (online)

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Summary

Objectives: We present an approach to time-frequency analysis of bioelectrical signals.

Methods: The method relays on the decomposition of the signal into a set of waveforms that have good localization both in time and in frequency. The waveforms belong to a highly redundant set of functions – allowing for a very accurate description of signal components.

Results: Properties of the method are illustrated by simulations and applications to EEG.

Conclusion: The presented method delivers a common formalism suitable for describing both gross statistical properties of structures present in bioelectrical signals, as well as microstructure of chosen phenomena.

Keywords

Time-frequency representations - adaptive approximations - matching pursuit - non-stationary signals - EEG - LFP
 

  • References

  • 1 Mallat SG, Zhang Z. Matching Pursuit with time-frequency dictionaries. IEEE Transactions on Signal Processing 1993; 41: 3397-415.
    CrossrefPubMedGoogle Scholar
  • 2 Blinowska KJ, Durka PJ. The Application of Wavelet Transform and Matching Pursuit to the Time-Varying EEG signals. In: Intelligent Engineering Systems through Artificial Neural Networks. Vol. 4. Dagli CH, Fernandez BR. (eds) ASME Press; New York: 1994: 535-540.
    Google Scholar
  • 3 Durka PJ, Ircha D, Blinowska KJ. Stochastic time-frequency dictionaries for Matching Pursuit. IEEE Transactions on Signal Processing, 2001; 49: 507-10.
    CrossrefPubMedGoogle Scholar
  • 4 Durka PJ, Blinowska KJ. Analysis of EEG transients by means of Matching Pursuit. Annals of Biomedical Engineering 1995; 23: 608-11.
    CrossrefPubMedGoogle Scholar
  • 5 Żygierewicz J, Blinowska KJ, Durka PJ, Szelenberger W, Niemcewicz S, Androsiuk W. High resolution study of sleep spindles. Clinical Neurophysiology 1999; 110: 2136-47.
    CrossrefPubMedGoogle Scholar
  • 6 Durka PJ, Ircha D, Neuper C, Pfurtscheller G. Time-frequency microstructure of event-related EEG desynchronisation and synchronisation. Med. & Biol. Engin. & Comput 2001; 39: 315-21.
    PubMedGoogle Scholar
  • 7 Ginter Jr. J, Blinowska KJ, Kaminski M, Durka PJ. Phase and amplitude analysis in timefrequency space – application to voluntary finger movement. J Neurosc Meth 2001; 110: 113-24.
    CrossrefPubMedGoogle Scholar
  • 8 Blinowska KJ, Durka PJ, Skierski A, Grandori F, Tognola G. High-resolution time-frequency analysis of otoacoustic emissions. Tech Health Care 1997; 5: 407-18.
    PubMedGoogle Scholar
  • 9 Żygierewicz J, Kelly EF, Blinowska KJ, Durka PJ, Folger SE. Time-Frequency Analysis of Vibrotactile Driving Responses by Matching Pursuit. J Neurosc Meth 1998; 81: 121-9.
    CrossrefPubMedGoogle Scholar
  • 10 Franaszczuk PJ, Bergey GK, Durka PJ, Eisenberg HM. Time-frequency analysis using the matching pursuit algorithm applied to seizures originating from the mesial temporal lobe. Electroenceph Clin Neurophys 1998; 106: 513-21.
    CrossrefPubMedGoogle Scholar