The efficacy of rehabilitative interventions in stroke patients is routinely assessed by
means of a neuropsychological test battery. Nowadays, more evidences indicate that the
neuroplasticity which occurs after stroke can be better understood by investigating
changes in brain networks. In this pilot study we applied advanced methodologies for
effective connectivity estimation in combination with graph theory approach, to define
EEG derived descriptors of brain networks underlying memory tasks. In particular, we
proposed such descriptors to identify substrates of efficacy of a Brain-Computer
Interface (BCI) controlled neurofeedback-based intervention to improve cognitive
function after stroke. EEG data were collected from two stroke patients before and after
a neurofeedback-based training for working memory deficits. We show that the
estimated brain connectivity indices were sensitive to different training intervention
outcomes, thus suggesting an effective support to the neuropsychological assessment in
the evaluation of the changes induced by the BCI-based rehabilitative intervention
