It make sense to calculate gray level dependend matrix on al time domain signal in order to extract features related to that matrix?
I have EEG signals from stroke people in the time domain. My goal is to classify from the signal the level of recovery from the stroke (I have also some scale to quantify the recovery).
I tried different classification pipelines but I achieved good results only extracting features related to the GLDM calculated directly from the time courses.
Since I'm no expert in texture analysis I was wondering if make sense to compute the GLDM matrix from a time varying signal instead an image.
I also tried to transform the time signal into a recurrence plot and then compute the GLDM but I didn't achieve good results.