  Decoding brain signals has gained many attention and has found much
applications in recent years such as Brain Computer Interfaces, communicating
with controlling external devices using the user's intentions, occupies an
emerging field with the potential of changing the world, with diverse
applications from rehabilitation to human augmentation. This being said brain
signal analysis, EEG brain signal analysis in particular, is a challenging
task. With the advances and achievements in the field of deep learning in
problem solving with using only raw data, few attempts has been carried in
recent years, to apply deep learning to tackle EEG among other types of brain
signals. In this study, we propose a novel loss function, called DeepCSP to
extend the classical Common Spatial Patterns to a non linear, differentiable
module to serve as the loss function to enforce linearly separable latent
representations of EEG signals belonging to different classes in an end to end
manner on raw signals without the need to perform extensive feature
engineering. With recent generalizations of deep learning methods to work on
arbitrarily structured graphs and the introduced loss we have proposed two
light weight models to decode EEG signals and carried experiments to show their
performance.
