Towards Ultrasound Tongue Image prediction from EEG during speech production

Previous initial research has already been carried out to propose speech-based BCI using brain signals (e.g.~non-invasive EEG and invasive sEEG / ECoG), but there is a lack of combined methods that investigate non-invasive brain, articulation, and speech signals together and analyze the cognitive processes in the brain, the kinematics of the articulatory movement and the resulting speech signal. In this paper, we describe our multimodal (electroencephalography, ultrasound tongue imaging, and speech) analysis and synthesis experiments, as a feasibility study. We extend the analysis of brain signals recorded during speech production with ultrasound-based articulation data. From the brain signal measured with EEG, we predict ultrasound images of the tongue with a fully connected deep neural network. The results show that there is a weak but noticeable relationship between EEG and ultrasound tongue images, i.e. the network can differentiate articulated speech and neutral tongue position.Comment: accepted at Interspeech 202

Hierarchical Deep Feature Learning For Decoding Imagined Speech From EEG

We propose a mixed deep neural network strategy, incorporating parallel combination of Convolutional (CNN) and Recurrent Neural Networks (RNN), cascaded with deep autoencoders and fully connected layers towards automatic identification of imagined speech from EEG. Instead of utilizing raw EEG channel data, we compute the joint variability of the channels in the form of a covariance matrix that provide spatio-temporal representations of EEG. The networks are trained hierarchically and the extracted features are passed onto the next network hierarchy until the final classification. Using a publicly available EEG based speech imagery database we demonstrate around 23.45% improvement of accuracy over the baseline method. Our approach demonstrates the promise of a mixed DNN approach for complex spatial-temporal classification problems.Comment: Accepted in AAAI 2019 under Student Abstract and Poster Progra