  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.
