Hello,
I have a iEEG data (ECoG) to preprocess, and I used MNE to get some results, but wanted to ask a few questions as this is my first time preprocessing electrophysiology data (I used to do MRI)
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It seems that the application of notch filters is not enough to remove the power line interference. As the picture below shows that by applying notch filters, 1. the higher frequencies (200Hz or more) actually become less than what is expected, 2. the interference at 60Hz is still persistent. (By the way, you can ignore the black and red lines, as they are just EKG signals) Is this normal? It seems that people can modify the notch filter width, should I do this?
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What is the optimal/minimal preprocessing that MUST be done to the data? As my goal is to create a deep learning model using large amounts of data, I feel that applying as little preprocessing as possible and keeping as much data as possible is the best approach (even at the cost of not removing some noise). Are there “critical” noise that is 99% noise and can be removed? For example, 1. should I do high pass filtering instead of band-pass filtering? 2. should I set the highness filter to be at 0.1 instead of 0.5 to preserve the data at 0.1~0.5Hz? Any input would be highly appreciated!
(notice that the picture below shows both the “original data” and "high pass filter at 0.5 Hz with notch filters at multiples of 60)
(code : )
raw_copy = raw.copy()
raw_copy.filter(l_freq=0.5, h_freq=250, verbose=False)
raw_copy.notch_filter(freqs=np.arange(60, 1000, 60), verbose=False)
Thank you in advance for any small reply 