I'm digging into some (quite popular in my field) analytical practice that I find suspicious. The problem is to remove the trend from some very short (eg. 18-40 data points representing 200 - 800 ms) time series. The common approach is to either use a moving average or a (something around 11-) order FIR filter. I already know that moving-avg is an "exceptionally bad low-pass filter ", but I'm concerned about potential artifacts that this procedure can introduce into the filtered signal. When I simulate filters used by other researchers with python-mne, I notice that the filter ringing takes many times longer than the signal they filtered. I have also heard about the existence of ringing artifacts and although from what I understand the problem is mainly about disturbing impulse and step signals, I wonder if this phenomenon can introduce artifacts in other types of signals as well. In particular, I have noticed that most filters in my field stop attenuating around 4 Hz, and a bit further, around 5 Hz, researchers often find an increase in frequency strength and then report the oscillating nature of the signal they are studying. I wonder this increase may be due to "overshoot" - like with ringing artifacts. I would love to understand this problem well, so please kindly explain to me if I am reasoning correctly and provide materials from which I could learn more about this phenomenon. Thank you very much in advance!
EDIT: To clarify: by the whole signal I mean an overall number of samples that were collected after a certain event, not that I slice a smaller subsample from a longer signal or I point out a number of non-zero values in a long time series.
: The Scientist and Engineer's Guide to Digital Signal Processing By Steven W. Smith, Ph.D.