  State-of-the-art brain-to-text systems have achieved great success in
decoding language directly from brain signals using neural networks. However,
current approaches are limited to small closed vocabularies which are far from
enough for natural communication. In addition, most of the high-performing
approaches require data from invasive devices (e.g., ECoG). In this paper, we
extend the problem to open vocabulary Electroencephalography(EEG)-To-Text
Sequence-To-Sequence decoding and zero-shot sentence sentiment classification
on natural reading tasks. We hypothesis that the human brain functions as a
special text encoder and propose a novel framework leveraging pre-trained
language models (e.g., BART). Our model achieves a 40.1% BLEU-1 score on
EEG-To-Text decoding and a 55.6% F1 score on zero-shot EEG-based ternary
sentiment classification, which significantly outperforms supervised baselines.
Furthermore, we show that our proposed model can handle data from various
subjects and sources, showing great potential for a high-performance open
vocabulary brain-to-text system once sufficient data is available
