In order to understand filtering, you need a basic understanding of Fourier Transforms.
Read An Introduction to the Event-related Potential Technique (Pg. 20-21) for a good introduction on filters.
A significant source of noise for an EEG study is the 60 Hz line noise from the electrical wires nearby. This noise needs to be filtered out.
Also, we need to filter out the very low (<0.01-0.1 Hz) and very high frequencies (>50-100 Hz) because these are definitely not brain data. Removing the very low frequencies takes out the slow drift of the data, and removing the very high frequencies helps reduce noise. I would recommend a
0.1 to 100 Hz filter
Filtering out too much of the data will significantly change the shape of the waveform, so it is necesary to filter out just enough to get rid of the noise, but not so much that the shape of the waveform is altered. Filtering out too many of the high frequencies will reduce the amplitudes of sharp peaks (such as the N170), which will skew the measurements of these amplitudes.
You also need to filter out the 60 Hz line noise. I would recommend a
58 to 62 Hz notch filter or 59 to 61 Hz notch filter
Here’s some data before and after the 60 Hz notch filter. I hid the channels above and below the red one so that you could see it more clearly.
Before 60 Hz Notch Filter:
After 60 Hz Notch Filter:
