  In this paper we demonstrate that it is possible to generate more meaningful
electroencephalography (EEG) features from raw EEG features using generative
adversarial networks (GAN) to improve the performance of EEG based continuous
speech recognition systems. We improve the results demonstrated by authors in
[1] using their data sets for for some of the test time experiments and for
other cases our results were comparable with theirs. Our proposed approach can
be implemented without using any additional sensor information, whereas in [1]
authors used additional features like acoustic or articulatory information to
improve the performance of EEG based continuous speech recognition systems.
