Robust Spectral Analysis of the EEG

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Abstract
Robust methods for the spectral analysis of time series are briefly reviewed and seen to have applications in the field of EEG. After presenting two simple schemes for outliers (artifacts) generation and discussing their implications for estimation of the spectral density, the robust filtering algorithm of Kleiner et al. [J. R. Statist. Soc. Ser. B, 41: 313-351, 1979] is introduced and shown to work well for simulated data and for true EEG data containing artifacts. A new use of the robust methods for the detection of artifacts and possibly other transients in long-time EEG recordings is suggested and a preliminary implementation illustrated.

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