  Typical EEG-based BCI applications require the computation of complex
functions over the noisy EEG channels to be carried out in an efficient way.
Deep learning algorithms are capable of learning flexible nonlinear functions
directly from data, and their constant processing latency is perfect for their
deployment into online BCI systems. However, it is crucial for the jitter of
the processing system to be as low as possible, in order to avoid unpredictable
behaviour that can ruin the system's overall usability. In this paper, we
present a novel encoding method, based on on deep convolutional autoencoders,
that is able to perform efficient compression of the raw EEG inputs. We deploy
our model in a ROS-Neuro node, thus making it suitable for the integration in
ROS-based BCI and robotic systems in real world scenarios. The experimental
results show that our system is capable to generate meaningful compressed
encoding preserving to original information contained in the raw input. They
also show that the ROS-Neuro node is able to produce such encodings at a steady
rate, with minimal jitter. We believe that our system can represent an
important step towards the development of an effective BCI processing pipeline
fully standardized in ROS-Neuro framework.
