Signal Averaging

Signal averaging is a signal processing technique applied in the time domain, intended to increase the strength of a signal relative to noise that is obscuring it.

1. How does signal averaging work?
2. What is signal averaging in EEG?
3. What is the importance of signal averaging?
4. Why does averaging reduce noise?

How does signal averaging work?

Signal averaging sums a set of time epochs of the signal together with the super- imposed random noise. If the time epochs are properly aligned, the signal wave- forms directly sum together. On the other hand, the uncorrelated noise averages out in time. Thus, the signal-to-noise ratio (SNR) is improved.

What is signal averaging in EEG?

The most commonly employed technique is signal averaging, whereby a number of EEG segments, each time-locked to the same class of event, are averaged to give a waveform in which consistent features of the segments (the ERP) are retained while features that vary across segments are attenuated (see Fig. 1).

What is the importance of signal averaging?

The ultimate reason to perform signal averaging is to increase the signal-to-noise ratio (Chapter 3). The estimate of residual noise can easily be established in a theoretical example illustrated in the simulation in pr4_1 where all the components are known.

Why does averaging reduce noise?

Averaging has the power to reduce noise without compromising detail, because it actually increases the signal to noise ratio (SNR) of your image. An added bonus is that averaging may also increase the bit depth of your image — beyond what would be possible with a single image.