Short-term variability in EEG frequency analysis

B. S. Oken, K. H. Chiappa

Research output: Contribution to journalArticlepeer-review

93 Scopus citations


Knowledge of short-term EEG variability in computerized analysis is important before interpreting spectral EEGs or assessing changes that may be due to inherent variability and not necessarily related to a task (e.g., listening to a story), therapy or changes in underlying disease. Eighty to 120 sec of 14-channel, edited, bipolar EEG were recorded in normal subjects and analyzed using an FFT. Absolute and relative power in 5 standard frequency bands, and median and peak power frequencies were obtained for each 4 sec epoch, and the mean and standard deviation calculated for each parameter. The average variation of the mean power, absolute and relative, in the frequency bands was less than 10% although some parameters varied by up to 50% in an individual subject. Median and peak power had the least variability, about 3%. Changes in total power correlated positively with relative alpha power, but negatively or not at all with the other relative power measures.This suggests that interpretation of relative measures of delta, theta and beta in individual spectra may be dependent on total power or absolute alpha power. In addition, mathematical transformations were necessary to normalize the epoch by epoch data, suggesting that the mean and standard deviation of data from a series of epochs may not have maximal value unless a transformation is used. These results also indicate that caution is needed in interpreting changes in EEG frequency analysis data that are of the same magnitude as spontaneous EEG variability.

Original languageEnglish (US)
Pages (from-to)191-198
Number of pages8
JournalElectroencephalography and Clinical Neurophysiology
Issue number3
StatePublished - Mar 1988
Externally publishedYes


  • EEG frequency analysis
  • Short-term variability

ASJC Scopus subject areas

  • Neuroscience(all)
  • Clinical Neurology


Dive into the research topics of 'Short-term variability in EEG frequency analysis'. Together they form a unique fingerprint.

Cite this