  Many recent studies have shown that the perception of speech can be decoded
from brain signals and subsequently reconstructed as continuous language.
However, there is a lack of neurological basis for how the semantic information
embedded within brain signals can be used more effectively to guide language
reconstruction. The theory of predictive coding suggests that human brain
naturally engages in continuously predicting future word representations that
span multiple timescales. This implies that the decoding of brain signals could
potentially be associated with a predictable future. To explore the predictive
coding theory within the context of language reconstruction, this paper
proposes a novel model \textsc{PredFT} for jointly modeling neural decoding and
brain prediction. It consists of a main decoding network for language
reconstruction and a side network for predictive coding. The side network
obtains brain predictive coding representation from related brain regions of
interest with a multi-head self-attention module. This representation is fused
into the main decoding network with cross-attention to facilitate the language
models' generation process. Experiments are conducted on the largest
naturalistic language comprehension fMRI dataset Narratives. \textsc{PredFT}
achieves current state-of-the-art decoding performance with a maximum BLEU-1
score of $27.8\%$.
