Dealing with non-stationarity and noise
The
EEG, especially when recorded from the scalp, is prone to many different artifacts which obstruct the view of underlying
brain activity.
#F9 shows some of these common artifacts. Most SDAs are based, at least in part, on detecting changes in the distribution of signal energy as a function of time and frequency (time-frequency-energy or “TFE” analysis).
One of the difficulties posed by artifacts/noise in the signal is that it can often overlap in the frequency domain with signals of interest (seizures). Also, the artifacts often come and go with time, as do seizures, making the degree of overlap at any given moment somewhat unpredictable. The underlying brain signals and their characteristics also change with time and state of the patient (i.e., they are non-stationary), which can pose a problem for certain popular conventional analysis techniques. Fourier analysis is perhaps the most common TFE analysis technique, but it has an underlying assumption that the signals being analyzed are stationary, which limits its utility for this application. Over the past two decades, wavelet-based TFE analysis and other related methods have become popular tools for use with EEG and other non-stationary signals, in part because their multi-resolution approach to TFE analysis deals with inherent non-stationarity in the signal more successfully (Schiff 1994, Osorio et al. 1998, Jouny et al. 2010). New methods for TFE analysis of non-stationary signals, such as Intrinsic Timescale Decomposition (ITD) (Frei and Osorio 2007) have recently provided additional tools to decompose these and other complex signals into components of interest, enabling separation of seizure/
epileptiform signal components from background/normal components.
