Robust power spectral estimation for EEG data.

TitleRobust power spectral estimation for EEG data.
Publication TypeJournal Article
Year of Publication2016
AuthorsMelman T, Victor JD
JournalJ Neurosci Methods
Date Published2016 08 01
KeywordsArtifacts, Bayes Theorem, Blinking, Brain, Computer Simulation, Data Interpretation, Statistical, Electroencephalography, Humans, Male, Models, Neurological, Signal Processing, Computer-Assisted, Software, Young Adult

BACKGROUND: Typical electroencephalogram (EEG) recordings often contain substantial artifact. These artifacts, often large and intermittent, can interfere with quantification of the EEG via its power spectrum. To reduce the impact of artifact, EEG records are typically cleaned by a preprocessing stage that removes individual segments or components of the recording. However, such preprocessing can introduce bias, discard available signal, and be labor-intensive. With this motivation, we present a method that uses robust statistics to reduce dependence on preprocessing by minimizing the effect of large intermittent outliers on the spectral estimates.

NEW METHOD: Using the multitaper method (Thomson, 1982) as a starting point, we replaced the final step of the standard power spectrum calculation with a quantile-based estimator, and the Jackknife approach to confidence intervals with a Bayesian approach. The method is implemented in provided MATLAB modules, which extend the widely used Chronux toolbox.

RESULTS: Using both simulated and human data, we show that in the presence of large intermittent outliers, the robust method produces improved estimates of the power spectrum, and that the Bayesian confidence intervals yield close-to-veridical coverage factors.

COMPARISON TO EXISTING METHOD: The robust method, as compared to the standard method, is less affected by artifact: inclusion of outliers produces fewer changes in the shape of the power spectrum as well as in the coverage factor.

CONCLUSION: In the presence of large intermittent outliers, the robust method can reduce dependence on data preprocessing as compared to standard methods of spectral estimation.

Alternate JournalJ. Neurosci. Methods
PubMed ID27102041
PubMed Central IDPMC4903894
Grant ListT32 GM083937 / GM / NIGMS NIH HHS / United States