The Practical Effect of Routine Data Transformations on Absolute EEG Power Derived from Spectral Analysis

Abstract

This investigation was conducted to evaluate the effects of selected EEG data transformations on analysis of variance (ANOVA) results in a repeated measures design. Eighteen subjects were given resting EEG evaluations at 8 different times during a period of continuous wakefulness. Following spectral analysis, the data were either analyzed as untransformed absolute EEG power, log-natural transformed power, or 2-arcsine-square root transformed relative power. Results indicated that while the relative power transformation lead to a more sensitive statistical analysis, it concurrently introduced data interpretation problems. In contrast, the results with both untransformed and transformed absolute power were quite similar. Overall, it was concluded that although transformations improve the Gaussian properties of the data, they do not appear to substantially impact the conclusions that will be drawn from a repeated measures.

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Document Details

Document Type
Technical Report
Publication Date
Nov 01, 1997
Accession Number
ADA332880

Entities

People

  • John A. Caldwell Jr.
  • Kristi A. Roberts

Organizations

  • United States Army Aeromedical Research Lab

Tags

Communities of Interest

  • Biomedical

DTIC Thesaurus Topics

  • Analysis Of Variance
  • Behavioral Sciences
  • Classification
  • Contrast
  • Data Science
  • Data Sets
  • Deprivation
  • Electrodes
  • Electroencephalography
  • Experimental Design
  • Information Science
  • Normal Distribution
  • Normality
  • Numbers
  • Square Roots
  • Statistical Analysis
  • Statistics

Readers

  • Approximation Theory.
  • Brain and Cognitive Science; Experimental Psychology; Cognitive Neuroscience
  • Regression Analysis.