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Previous initial research has already been carried out to proposespeech-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-invasivebrain, articulation, and speech signals together and analyze the cognitiveprocesses in the brain, the kinematics of the articulatory movement and theresulting speech signal. In this paper, we describe our multimodal(electroencephalography, ultrasound tongue imaging, and speech) analysis andsynthesis experiments, as a feasibility study. We extend the analysis of brainsignals recorded during speech production with ultrasound-based articulationdata. From the brain signal measured with EEG, we predict ultrasound images ofthe tongue with a fully connected deep neural network. The results show thatthere is a weak but noticeable relationship between EEG and ultrasound tongueimages, i.e. the network can differentiate articulated speech and neutraltongue position.

Towards Ultrasound Tongue Image prediction from EEG during speech production