Time-Frequency Analysis

  • Walter J. Freeman
  • Rodrigo Quian Quiroga


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.


Power Spectrum Epileptic Seizure Uncertainty Principle Shannon Entropy Frequency Resolution 
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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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