Discriminating mental states from brain signals is crucial for many applications in cognitive
and clinical neuroscience. Most of the studies relied on the feature extraction from the
activity of single brain areas, thus neglecting the potential contribution of their functional
coupling, or connectivity. Here, we consider spectral coherence and imaginary coherence to
infer brain connectivity networks from electroencephalographic (EEG) signals recorded
during motor imagery and resting states in a group of healthy subjects. By using a graph …