  Speech-related Brain Computer Interface (BCI) technologies provide effective
vocal communication strategies for controlling devices through speech commands
interpreted from brain signals. In order to infer imagined speech from active
thoughts, we propose a novel hierarchical deep learning BCI system for
subject-independent classification of 11 speech tokens including phonemes and
words. Our novel approach exploits predicted articulatory information of six
phonological categories (e.g., nasal, bilabial) as an intermediate step for
classifying the phonemes and words, thereby finding discriminative signal
responsible for natural speech synthesis. The proposed network is composed of
hierarchical combination of spatial and temporal CNN cascaded with a deep
autoencoder. Our best models on the KARA database achieve an average accuracy
of 83.42% across the six different binary phonological classification tasks,
and 53.36% for the individual token identification task, significantly
outperforming our baselines. Ultimately, our work suggests the possible
existence of a brain imagery footprint for the underlying articulatory movement
related to different sounds that can be used to aid imagined speech decoding.
