I'm trying to predict Freezing of Gait (FoG) for Parkinson's patients using EMG signals recorded from three types of muscles of the subjects - tibialis anterior muscle of right leg, gastrocnemius muscle of right leg, and tibialis anterior muscle of left leg. It's a two class classification problem.
Should I concatenate the data of these three columns to a single column before applying some window function to them? Or should I store the data from these three muscles in three different columns and process them separately because data from different muscles have different distributions hence putting them in a single column may confuse the deep learning model we are going to build?
I've shared signals (4000 samples) from three muscles for a particular subject below: