vEEGNet: learning latent representations to reconstruct EEG raw data via
  variational autoencoders
Alberto Zancanaro Giulia Cisotto Italo Zoppis Sara Lucia Manzoni
Electroencephalografic (EEG) data are complex multi-dimensional time-series
that are very useful in many applications, from diagnostics to driving
brain-computer interface systems. Their classification is still a challenging
task, due to the inherent within- and between-subject variability and their low
signal-to-noise ratio. On the other hand, the reconstruction of raw EEG data is
even more difficult because of the high temporal resolution of these signals.
Recent literature has proposed numerous machine and deep learning models that
could classify, e.g., different types of movements, with an accuracy in the
range 70% to 80% (with 4 classes). On the other hand, a limited number of works
targeted the reconstruction problem, with very limited results. In this work,
we propose vEEGNet, a DL architecture with two modules, i.e., an unsupervised
module based on variational autoencoders to extract a latent representation of
the data, and a supervised module based on a feed-forward neural network to
classify different movements. To build the encoder and the decoder of VAE we
exploited the well-known EEGNet network. We implemented two slightly different
architectures of vEEGNet, thus showing state-of-the-art classification
performance, and the ability to reconstruct both low-frequency and middle-range
components of the raw EEG. Although preliminary, this work is promising as we
found out that the low-frequency reconstructed signals are consistent with the
so-called motor-related cortical potentials, well-known motor-related EEG
patterns and we could improve over previous literature by reconstructing faster
EEG components, too. Further investigations are needed to explore the
potentialities of vEEGNet in reconstructing the full EEG data, generating new
samples, and studying the relationship between classification and
reconstruction performance.