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

Open Vocabulary Electroencephalography-To-Text Decoding and Zero-shot Sentiment Classification