Chapter 9: Overview of Time-Domain EEG Analysis

This chapter discusses various aspect of analysis of Time-Series data, particularly in regards to the ERP

9.1 Event-Related Potentials

Since noise is theoretically random and signal isn't. If you take a bunch of trials, phase lock them at a t = 0 event and average them, the noise gets attenuated and the signal remaining is the ERP. This much knowledge is sufficient when using ERP for data inspection. For making inferences on cognitive processes, be wary, and research component overlap, component quantification, appropriate interpretation, and statistical procedures first.

[FIgure 9.1]

9.2 Filtering ERPs

Time domain signal averaging can act as a low pass filter. One way this happens is with non-phased locked activity, which is lost during averaging because it inst locked to a phase, non-phased locked activities in the brain tend to be above 15 Hz. In addition, short high frequency events often jitter temporally between subjects, and thus like non-phase locked activity get lost during averaging.

Further filtering frequencies from ERPs is not always necessary, and is a debated topic. The benefits it can bring include reducing residual high frequency fluctuations, which reduces the possibility that a peak is only a noise spike. However there are also risks that a poorly executed filter results in ringing artifacts, as seen in the following figure with over-zealous 0-10 Hz and 5-15hz band-pass filters. Meanwhile the 0-40 Hz filter much closer resembles the raw data.

[Figure 9.2]

The particular danger with these ringing artifacts is that they can appear to be oscillations, which can lead to incorrect conclusions in your research.

Ripple artifacts can largely be avoided by constructing filters with gentle transition zones. Chapter 14 discusses proper filter construction to avoid this and other artifacts.

Another more fundamental issue with filtering ERPs is that low pass filters reduce temporal resolution of the data, which is arguably one of the main points of the ERP, and this effect becomes more pronounced the lower the top frequency cutoff is in the band-pass filer.

When filtered, generally the high end is ~20-30 Hz and the low end is ~5-10 Hz. Note that mathematically speaking, applying the same filter to all trials then averaging them is the same as averaging all the trials then applying the filter to it.