### Abstract

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 language | English (US) |
---|---|

Pages (from-to) | 191-198 |

Number of pages | 8 |

Journal | Electroencephalography and Clinical Neurophysiology |

Volume | 69 |

Issue number | 3 |

DOIs | |

State | Published - 1988 |

Externally published | Yes |

### Fingerprint

### Keywords

- EEG frequency analysis
- Short-term variability

### ASJC Scopus subject areas

- Clinical Neurology
- Neuroscience(all)

### Cite this

*Electroencephalography and Clinical Neurophysiology*,

*69*(3), 191-198. https://doi.org/10.1016/0013-4694(88)90128-9

**Short-term variability in EEG frequency analysis.** / Oken, Barry; Chiappa, K. H.

Research output: Contribution to journal › Article

*Electroencephalography and Clinical Neurophysiology*, vol. 69, no. 3, pp. 191-198. https://doi.org/10.1016/0013-4694(88)90128-9

}

TY - JOUR

T1 - Short-term variability in EEG frequency analysis

AU - Oken, Barry

AU - Chiappa, K. H.

PY - 1988

Y1 - 1988

N2 - 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.

AB - 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.

KW - EEG frequency analysis

KW - Short-term variability

UR - http://www.scopus.com/inward/record.url?scp=0023855051&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=0023855051&partnerID=8YFLogxK

U2 - 10.1016/0013-4694(88)90128-9

DO - 10.1016/0013-4694(88)90128-9

M3 - Article

C2 - 2450000

AN - SCOPUS:0023855051

VL - 69

SP - 191

EP - 198

JO - Electroencephalography and Clinical Neurophysiology

JF - Electroencephalography and Clinical Neurophysiology

SN - 0013-4694

IS - 3

ER -