  Human-machine interactions are being increasingly explored to create
alternative ways of communication and to improve our daily life. Based on a
classification of the user's intention from the user's underlying neural
activity, brain-computer interfaces (BCIs) allow direct interactions with the
external environment while bypassing the traditional effector of the
musculoskeletal system. Despite the enormous potential of BCIs, there are still
a number of challenges that limit their societal impact, ranging from the
correct decoding of a human's thoughts, to the application of effective
learning strategies. Despite several important engineering advances, the basic
neuroscience behind these challenges remains poorly explored. Indeed, BCIs
involve complex dynamic changes related to neural plasticity at a diverse range
of spatiotemporal scales. One promising antidote to this complexity lies in
network science, which provides a natural language in which to model the
organizational principles of brain architecture and function as manifest in its
interconnectivity. Here, we briefly review the main limitations currently
affecting BCIs, and we offer our perspective on how they can be addressed by
means of network theoretic approaches. We posit that the emerging field of
network neuroscience will prove to be an effective tool to unlock human-machine
interactions.
