Studying the variability of data is of special interest because patterns in variance are usually good signals to be considered to discriminate between subjects.
principal Component Analysis (pCA) (Jolliffe, 2002) will be used to obtain the directions in the feature space where the covariance matrix of the observations varies the most, as given by the eigen-vectors associated to the highest absolute eigen-values.