  New trends on brain-computer interface (BCI) design are aiming to combine
this technology with immersive virtual reality in order to provide a sense of
realism to its users. In this study, we propose an experimental BCI to control
an immersive telepresence system using motor imagery (MI). The system is
immersive in the sense that the users can control the movement of a NAO
humanoid robot in a first person perspective (1PP), i.e., as if the movement of
the robot was his/her own. We analyze functional brain connectivity between 1PP
and 3PP during the control of our BCI using graph theory properties such as
degree, betweenness centrality, and efficiency. Changes in these metrics are
obtained for the case of the 1PP, as well as for the traditional third person
perspective (3PP) in which the user can see the movement of the robot as
feedback. As proof-of-concept, electroencephalography (EEG) signals were
recorded from two subjects while they performed MI to control the movement of
the robot. The graph theoretical analysis was applied to the binary directed
networks obtained through the partial directed coherence (PDC). In our
preliminary assessment we found that the efficiency in the alpha brain rhythm
is greater in 1PP condition in comparison to the 3PP at the prefrontal cortex.
Also, a stronger influence of signals measured at EEG channel C3 (primary motor
cortex) to other regions was found in 1PP condition. Furthermore, our
preliminary results seem to indicate that alpha and beta brain rhythms have a
high indegree at prefrontal cortex in 1PP condition, and this could be possibly
related to the experience of sense of agency. Therefore, using the PDC combined
with graph theory while controlling a telepresence robot in an immersive system
may contribute to understand the organization and behavior of brain networks in
these environments.
