  In this paper we demonstrate that performance of voice activity detection
(VAD) system operating in presence of background noise can be improved by
concatenating acoustic input features with electroencephalography (EEG)
features. We also demonstrate that VAD using only EEG features shows better
performance than VAD using only acoustic features in presence of background
noise. We implemented a recurrent neural network (RNN) based VAD system and we
demonstrate our results for two different data sets recorded in presence of
different noise conditions in this paper. We finally demonstrate the ability to
predict whether a person wish to continue speaking a sentence or not from EEG
features.
