  Language, as an information medium created by advanced organisms, has always
been a concern of neuroscience regarding how it is represented in the brain.
Decoding linguistic representations in the evoked brain has shown
groundbreaking achievements, thanks to the rapid improvement of neuroimaging,
medical technology, life sciences and artificial intelligence. In this work, we
present a taxonomy of brain-to-language decoding of both textual and speech
formats. This work integrates two types of research: neuroscience focusing on
language understanding and deep learning-based brain decoding. Generating
discernible language information from brain activity could not only help those
with limited articulation, especially amyotrophic lateral sclerosis (ALS)
patients but also open up a new way for the next generation's brain-computer
interface (BCI). This article will help brain scientists and deep-learning
researchers to gain a bird's eye view of fine-grained language perception, and
thus facilitate their further investigation and research of neural process and
language decoding.
