I have a set of 10 minute EEG signals that were sampled at 400 Hz and have 16 channels which corresponds to a 16x240000 matrix. These EEG signals belong to two different classes. I am trying to classify these 10 minute segments using a Neural Network, in particular a LSTM.
Since the size of the matrix is very large, even after I split them into time segments, I would like to first reduce the number of samples. My current approach for pre-processing is to use wavelet transform for denosing and down sample to at least 200 Hz. This results in a 16x1200 matrix.
I was also looking at ICA as a feature extractor and reduce the size.
My question is, how do I tell which method is better suited for my task? Will downsampling the signal lead to a significant loss in information?
I will appreciate any suggestions. Thank you.
Edit: - The data corresponds to two stages of seizure:
Preictal: Right before the seizure
Interictal: in between seizure
-The data was captured from 3 different patients and using 16 electrodes, sampled at 400Hz.