For many people suffering from motor disabilities,
assistive devices controlled with only brain activity are the
only way to interact with their environment [1]. Natural
tasks often require different kinds of interactions, involving
different controllers the user should be able to select in a
self-paced way. We developed a Brain-Computer Interface
(BCI) allowing users to switch between four control modes
in a self-paced way in real-time. Since the system is devised
to be used in domestic environments in a user-friendly way,
we selected non-invasive electroencephalographic (EEG) signals
and convolutional neural networks (CNNs), known for their
ability to find the optimal features in classification tasks. We
tested our system using the Cybathlon BCI computer game,
which embodies all the challenges inherent to real-time control.
Our preliminary results show that an efficient architecture
(SmallNet), with only one convolutional layer, can classify 4
mental activities chosen by the user. The BCI system is run and
validated online. It is kept up-to-date through the use of newly
collected signals along playing, reaching an online accuracy
of
47
.
6%
where most approaches only report results obtained
offline. We found that models trained with data collected online
better predicted the behaviour of the system in real-time. This
suggests that similar (CNN based) offline classifying methods
found in the literature might experience a drop in performance
when applied online. Compared to our previous decoder of
physiological signals relying on blinks, we increased by a factor
2 the amount of states among which the user can transit,
bringing the opportunity for finer control of specific subtasks
composing natural grasping in a self-paced way. Our results
are comparable to those showed at the Cybathlon’s BCI Race
but further improvements on accuracy are required.