Spectral analysis methods for neurological signals

Jitendran Muthuswamy, Nitish V. Thakor

Research output: Contribution to journalArticle

144 Scopus citations

Abstract

This paper reviews some novel spectral analysis techniques that are useful for neurological signals in general and EEG signals in particular. First, some drawbacks and limitations of the commonly used Fast Fourier transforms (FFTs) are presented, and then alternative algorithms are outlined. An auto-regressive (AR) modeling based spectral estimation procedure is presented to overcome the problems of lower resolution and 'leakage' effects inherent in the FFT algorithm. For signals which are transient in nature or rapidly time-varying, two alternative algorithms are presented. The first is an adaptive AR parameter estimation algorithm and the second is a wavelet based time-frequency representation algorithm. Finally, a Spectral Distance measure and the Itakura distance measure are presented to quantify the differences between the spectra of two signals in a succinct manner. The application and performance of all the algorithms is illustrated using electroencephalograms (EEGS) recorded in animals during hypoxic- asphyxic injury to brain.

Original languageEnglish (US)
Pages (from-to)1-14
Number of pages14
JournalJournal of Neuroscience Methods
Volume83
Issue number1
DOIs
StatePublished - Aug 31 1998
Externally publishedYes

Keywords

  • AR modeling
  • Adaptive AR
  • EEG
  • FFT
  • Wavelets

ASJC Scopus subject areas

  • Neuroscience(all)

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