  Deciphering the intricacies of the human brain has captivated curiosity for
centuries. Recent strides in Brain-Computer Interface (BCI) technology,
particularly using motor imagery, have restored motor functions such as
reaching, grasping, and walking in paralyzed individuals. However, unraveling
natural language from brain signals remains a formidable challenge.
Electroencephalography (EEG) is a non-invasive technique used to record
electrical activity in the brain by placing electrodes on the scalp. Previous
studies of EEG-to-text decoding have achieved high accuracy on small closed
vocabularies, but still fall short of high accuracy when dealing with large
open vocabularies. We propose a novel method, EEG2TEXT, to improve the accuracy
of open vocabulary EEG-to-text decoding. Specifically, EEG2TEXT leverages EEG
pre-training to enhance the learning of semantics from EEG signals and proposes
a multi-view transformer to model the EEG signal processing by different
spatial regions of the brain. Experiments show that EEG2TEXT has superior
performance, outperforming the state-of-the-art baseline methods by a large
margin of up to 5% in absolute BLEU and ROUGE scores. EEG2TEXT shows great
potential for a high-performance open-vocabulary brain-to-text system to
facilitate communication.
