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Time-Frequency Analysis

  • Walter J. Freeman
  • Rodrigo Quian Quiroga
Chapter

Abstract

In the previous chapter, we mentioned that one of the main limitations of the Fourier transform is that it does not have time resolution. For calculating the Fourier transform, we assume that the signal is stationary and, consequently, that the activity at different frequencies is constant throughout the whole signal. In many occasions, however, signals have time-varying features that cannot be resolved with the Fourier transform. This is the case of music, speech, animal sounds, radar data, and many other signals (see examples in Cohen 1995). For EEG signals, this limitation is critical when we analyze processes that change in time, such as the response to a particular stimulus or the development of an epileptic seizure.

Keywords

Power Spectrum Epileptic Seizure Uncertainty Principle Shannon Entropy Frequency Resolution 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

References

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Copyright information

© Springer Science+Business Media New York 2013

Authors and Affiliations

  • Walter J. Freeman
    • 1
  • Rodrigo Quian Quiroga
    • 2
  1. 1.Molecular and Cell BiologyUniversity of California at BerkeleyBerkeleyUSA
  2. 2.Centre for Systems NeuroscienceUniversity of LeicesterLeicesterUK

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