How do you denoise and extract features from EEG data with Python? I have EEG data with 5 columns (1 per each electrode) and I need to denoise it and extract features from it using Python. I tried to find relevant packages but my search kept leading me to MNE which takes as input data in a format that I don't have. My data is in a pandas dataframe.

my questions are:


How do you denoise such dataset? 
How do you do feature extraction for it?


Thanks :)
you can convert your data frame to numpy array using 

data=df.to_numpy()


If your data is single trial meanining two dimensional dataset, you can use

channel_names=df.columns.tolist()
channel_types=len(channel_names)*['eeg']
sfreq = 1000  # in Hertz
montage = 'standard_1005'
info = mne.create_info(channel_names, sfreq, channel_types, montage)
raw = mne.io.RawArray(data, info)


If you have multitrials dataset

channel_names=df.columns.tolist()
channel_types=len(channel_names)*['eeg']
sfreq = 1000  # in Hertz
montage = 'standard_1005'
info = mne.create_info(channel_names, sfreq, channel_types, montage)
data=data.reshape(len(channel_names),-1,sfreq*time)
data=np.swapaxes(data,0,1)
epochs = mne.EpochsArray(sub, info)


Once you convert your dataset to mne structure, you can use mne filters and mne features