  In this paper we demonstrate predicting electroencephalograpgy (EEG) features
from acoustic features using recurrent neural network (RNN) based regression
model and generative adversarial network (GAN). We predict various types of EEG
features from acoustic features. We compare our results with the previously
studied problem on speech synthesis using EEG and our results demonstrate that
EEG features can be generated from acoustic features with lower root mean
square error (RMSE), normalized RMSE values compared to generating acoustic
features from EEG features (ie: speech synthesis using EEG) when tested using
the same data sets.
