How do you do signal averaging on a realtime data?

1. How does signal averaging work?
2. What is EEG signal averaging?
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 EEG signal averaging?

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?

Hence, we can conclude that signal averaging can increase the signal to noise ratio (SNR) by a factor of M. This analysis allows us to choose the desired number of experiments for the averaging technique so that we can achieve an arbitrary SNR.

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.