Fig. 2.

Flow diagram of the classifier pipeline. We trained the classifier with EEG data from the wakeful imagery task (bluish colours), next we used EEG data from sleep (orange colours) to feed the trained algorithm and calculate the final accuracy results (purple colours). From the imagery data we extracted 3 types of features (temporal, spectral and wavelet-based features) that divided into training and testing sets were used to train the classifier after a selection process to reduce the number of features. The ranking and selection of features was done using join mutual information (JMI) algorithm and a wrapping methodology. Once the classifier (LDC) was trained we extracted the same type of features from the sleep dataset and used them to feed the trained classifier. An additional control step (permutation of labels) was added to be sure that the classification rates were not due merely to the chance probability.