Given some neurophysiological application I am filtering in real time data of limited length (e.g. in epochs of 100 ms) using a sampling rate of e.g. 100 Hz.
Due to this short time segment I am trying to implement a FIR filter. This filter should be of high order, and therefore have a length of more than 100 samples.
This would lead to the fact that the "to be filtered signal" has a different length than the estimated filter itself. Using the scipy.convolve function I can then specify different methods how to convolve the signal (full, valid, same).
My question is quite trivial, and related to lack of experience in the field, but does it make sense at all to have different filter and signal length? Is there literature how to interpret then the filtered signal? Since the convolution needs to define some padding for the signal itself the filtered signal should be only in a certain range interpretable right?
EDIT: Hereby I want to present an example. I plot a random signal in orange, and a FIR filter with a filter length of 10 s (estimated using mne.create_filter):
My understanding problem is visualized in this context: the signal itself is much shorter than the filter. This leads obviously to the fact that the filtered signal has a longer length. How can I now interprete the filtered signal with respect to the timing of the original signal that had fs=128 Hz? Which time points of the filtered signal correspond to the original signal?