Robust power spectral estimation for EEG

Robust power spectral estimation for EEG data

Tamar Melman and Jonathan D. Victor

Journal of Neuroscience Methods 268, 14-22 (2016)


Background: Typical electroencephalogram (EEG) recordings often contain substantial artifact. These arti-facts, 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 removesindividual 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 usesrobust statistics to reduce dependence on preprocessing by minimizing the effect of large intermittentoutliers 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 Jack knife approach to confidence intervals with a Bayesian approach. The method is implemented in provided MATLABmodules, 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 confidenceintervals yield close-to-veridical coverage factors.

Comparison to existing method: The robust method, as compared to the standard method, is less affectedby artifact: inclusion of outliers produces fewer changes in the shape of the power spectrum as well asin the coverage factor.

Conclusion: In the presence of large intermittent outliers, the robust method can reduce dependence ondata preprocessing as compared to standard methods of spectral estimation

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