Generating EEG features from Acoustic features
This work addresses the problem of cross-modal feature generation in brain-computer interfaces, but it appears incremental as it builds on existing methods for EEG-acoustic relationships.
The paper tackled predicting EEG features from acoustic features using RNN and GAN models, achieving lower RMSE and normalized RMSE values compared to prior work on speech synthesis from EEG features.
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.