  In this paper we introduce a recurrent neural network (RNN) based variational
autoencoder (VAE) model with a new constrained loss function that can generate
more meaningful electroencephalography (EEG) features from raw EEG features to
improve the performance of EEG based speech recognition systems. We demonstrate
that both continuous and isolated speech recognition systems trained and tested
using EEG features generated from raw EEG features using our VAE model results
in improved performance and we demonstrate our results for a limited English
vocabulary consisting of 30 unique sentences for continuous speech recognition
and for an English vocabulary consisting of 2 unique sentences for isolated
speech recognition. We compare our method with another recently introduced
method described by authors in [1] to improve the performance of EEG based
continuous speech recognition systems and we demonstrate that our method
outperforms their method as vocabulary size increases when trained and tested
using the same data set. Even though we demonstrate results only for automatic
speech recognition (ASR) experiments in this paper, the proposed VAE model with
constrained loss function can be extended to a variety of other EEG based brain
computer interface (BCI) applications.
