  The identification of intentionally delivered commands is a challenge in
Brain Computer Interfaces (BCIs) based on Sensory-Motor Rhythms (SMR). It is of
fundamental importance that BCI systems controlling a robotic device (i.e.,
upper limb prosthesis) are capable of detecting if the user is in the so called
Intentional Non-Control (INC) state (i.e., holding the prosthesis in a given
position). In this work, we propose a novel approach based on the entropy of
the Electroencephalogram (EEG) signals to provide a continuous identification
of motion intention. Results from ten healthy subjects suggest that the
proposed system can be used for reliably predicting motion in real-time at a
framerate of 8 Hz with $80\% \pm 5\%$ of accuracy. Moreover, motion intention
can be detected more than 1 second before muscular activation with an average
accuracy of $76\% \pm 11\%$.
