Abstract
Many model-based methods have been developed over the last several decades for analysis of electroencephalograms (EEGs) in order to understand electrical neural data. In this work, we propose to use the functional boxplot (FBP) to analyze log periodograms of EEG time series data in the spectral domain. The functional bloxplot approach produces a median curve—which is not equivalent to connecting medians obtained from frequency-specific boxplots. In addition, this approach identifies a functional median, summarizes variability, and detects potential outliers. By extending FBPs analysis from one-dimensional curves to surfaces, surface boxplots are also used to explore the variation of the spectral power for the alpha (8–12 Hz) and beta (16–32 Hz) frequency bands across the brain cortical surface. By using rank-based nonparametric tests, we also investigate the stationarity of EEG traces across an exam acquired during resting-state by comparing the spectrum during the early vs. late phases of a single resting-state EEG exam.
Highlights
Electroencephalograms (EEGs) have been used for many decades to study the complex spatiotemporal dynamics of brain processes (Nunez and Srinivasan, 2006)
This study has extended the use of the classical boxplot to functional boxplot (FBP), which is a new visualization tool to analyze functional neuroimaging data, including EEG
The primary findings from the current study demonstrate the FBP is useful for both characterizing the spectral distribution of both simulated and real EEG data and identifying potential outliers in a continuous EEG signal
Summary
Electroencephalograms (EEGs) have been used for many decades to study the complex spatiotemporal dynamics of brain processes (Nunez and Srinivasan, 2006). Since EEGs indirectly measure neuronal electrical activity, they can be used to infer the statistical properties of the underlying brain stochastic process. One such statistical property is the spectrum (or power spectrum) which decomposes the total variability in the EEG according to the contribution of oscillations at different frequencies. Most approaches to analyzing EEGs focus immediately on statistical modeling and spectral estimation. We explore the spectral properties only in a single channel using EEG traces from several epochs
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