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An exploratory data analysis of electroencephalograms using the functional boxplots approach.

Duy Ngo1, Ying Sun2, Marc G Genton2

  • 1Department of Statistics, University of California, Irvine Irvine, CA, USA.

Frontiers in Neuroscience
|September 9, 2015
PubMed
Summary

This study introduces functional boxplots (FBPs) for analyzing electroencephalogram (EEG) spectral data. FBPs offer a novel method for understanding neural data variability and detecting outliers in EEG signals.

Keywords:
EEGs time seriesband depthexploratory analysisfunctional boxplotsspectral analysisstationaritysurface boxplots

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Area of Science:

  • Neuroscience
  • Statistics
  • Signal Processing

Background:

  • Electroencephalograms (EEGs) are crucial for understanding neural electrical activity.
  • Traditional model-based methods for EEG analysis are numerous.
  • A need exists for advanced statistical tools to analyze complex EEG data.

Purpose of the Study:

  • To introduce and apply the functional boxplot (FBP) for analyzing EEG time series data in the spectral domain.
  • To extend FBP to surface analysis for exploring spectral power variations across the brain's cortical surface.
  • To investigate the stationarity of resting-state EEG signals using nonparametric tests.

Main Methods:

  • Analysis of log periodograms of EEG time series data using functional boxplots (FBPs).
  • Extension of FBPs to surface boxplots for analyzing spectral power in alpha and beta frequency bands.
  • Application of rank-based nonparametric tests to compare early and late phases of resting-state EEG exams.

Main Results:

  • The functional boxplot approach yields a unique median curve and summarizes data variability.
  • Surface boxplots effectively explore spectral power variations across the cortical surface for specific frequency bands.
  • Nonparametric tests provide insights into the stationarity of resting-state EEG signals.

Conclusions:

  • Functional boxplots offer a robust method for analyzing spectral properties of EEG data.
  • Surface boxplots enhance the exploration of spatial variations in neural oscillations.
  • The study demonstrates a novel approach to assess EEG signal stationarity during resting states.