  Decoding language representations directly from the brain can enable new
Brain-Computer Interfaces (BCI) for high bandwidth human-human and
human-machine communication. Clinically, such technologies can restore
communication in people with neurological conditions affecting their ability to
speak. In this study, we propose a novel deep network architecture Brain2Char,
for directly decoding text (specifically character sequences) from direct brain
recordings (called Electrocorticography, ECoG). Brain2Char framework combines
state-of-the-art deep learning modules --- 3D Inception layers for multiband
spatiotemporal feature extraction from neural data and bidirectional recurrent
layers, dilated convolution layers followed by language model weighted beam
search to decode character sequences, optimizing a connectionist temporal
classification (CTC) loss. Additionally, given the highly non-linear
transformations that underlie the conversion of cortical function to character
sequences, we perform regularizations on the network's latent representations
motivated by insights into cortical encoding of speech production and
artifactual aspects specific to ECoG data acquisition. To do this, we impose
auxiliary losses on latent representations for articulatory movements, speech
acoustics and session specific non-linearities. In 3 participants tested here,
Brain2Char achieves 10.6\%, 8.5\% and 7.0\% Word Error Rates (WER) respectively
on vocabulary sizes ranging from 1200 to 1900 words. Brain2Char also performs
well when 2 participants silently mimed sentences. These results set a new
state-of-the-art on decoding text from brain and demonstrate the potential of
Brain2Char as a high-performance communication BCI.
