Treatment of Some Nonstationarities in the EEG

Abstract

In many situations, EEG recordings cannot be assumed to be second-order stationary. The definition of stationarity is reviewed and the implications of the nonstationarity of the EEG are investigated. Some methods to overcome the problem caused by the nonstationarity are discussed. They include measures of variability, condensed time series and segmentation. The discussion is restricted to FFT spectral estimators and broad-band parameters derived thereof.

From EEG dependency multichannel matching pursuit to sparse topographic EEG decomposition

“…The choice of Gabor elements as atoms of the dictionary matches well with many needs of EEG analysis: They are well defined in frequency and thus cover a domain where most of the analyses of continuous EEG data have been made and where there is a vast body of literature that can be referred to (see Section 1). On the other side, they reduce the assumption of stationarity to small, subsecond time-windows, which is probably more appropriate to ongoing brain activity than assuming stationarity over the entire analysis period (Ferber, 1987;Gonzalez Andino et al, 2001). Furthermore, they correspond well to the so-called spindles that are often used in visual descriptions of EEG.…”

Non-Parametric Statistical Diagnosis

“…The separate detection of changes in different frequency bands and with different time scales was already used by other researchers [95,109]. In our work this was done with a more broad range of signal types, covering a substantial portion of the variety of patterns occurring in the EEG studies.…”

Improving session-to-session transfer performance of motor imagery-based BCI using adaptive extreme learning machine

“…Therefore, applying methods which aimed to reduce such differences between train and evaluation data are useful. Several studies proposed methods to address non-stationarity of the EEG signal so far [6][7][8][9][10][11]. General speaking, adaptation is the most commonly used method to address nonstationarity.…”