Convolutional Neural Networks (CNN) outperform traditional classification
methods in many domains. Recently these methods have gained attention in
neuroscience and particularly in brain-computer interface (BCI) community...
Here, we introduce a CNN optimized for classification of brain states from
magnetoencephalographic (MEG) measurements. Our CNN design is based on a
generative model of the electromagnetic (EEG and MEG) brain signals and is
readily interpretable in neurophysiological terms. We show here that the
proposed network is able to decode event-related responses as well as
modulations of oscillatory brain activity and that it outperforms more complex
neural networks and traditional classifiers used in the field. Importantly, the
model is robust to inter-individual differences and can successfully generalize
to new subjects in offline and online classification.