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

Predicting Different Acoustic Features from EEG and towards direct synthesis of Audio Waveform from EEG