The reconfiguration of large-scale interactions among multiple brain regions is characterized by aperiodic perturbations, called “Neuronal Avalanches”, which can be tracked non-invasively as they expand across the brain. As such, learning a new task might affect the path of propagation of neuronal avalanches. Brain-Computer Interfaces (BCIs) constitute a promising tool for establishing direct communication and control from the brain over external effectors but mastering them remains a poorly-understood learned skill. Therefore, neuronal avalanche measures may constitute natural candidates to inform the underlying brain processes and their reflection on brain signals. To test this hypothesis, we used source-reconstructed magneto/electroencephalography, comparing resting-state to motor imagery conditions during a BCI protocol. For each experimental condition, we computed an individual avalanche transition matrix, to track the probability that an avalanche would spread across any two regions. We found a robust topography of the edges that were affected by the execution of the task, which mainly hinge upon the premotor regions. Finally, we related the individual differences to the task performance, showing that significant correlations are predominantly positive and involve edges connecting pre/motor regions to parietal ones. Our findings suggest that avalanches capture functionally-relevant processes crucial for alternative BCI designing.