  In [1,2] authors provided preliminary results for synthesizing speech from
electroencephalography (EEG) features where they first predict acoustic
features from EEG features and then the speech is reconstructed from the
predicted acoustic features using griffin lim reconstruction algorithm. In this
paper we first introduce a deep learning model that takes raw EEG waveform
signals as input and directly produces audio waveform as output. We then
demonstrate predicting 16 different acoustic features from EEG features. We
demonstrate our results for both spoken and listen condition in this paper. The
results presented in this paper shows how different acoustic features are
related to non-invasive neural EEG signals recorded during speech perception
and production.
