Open Vocabulary Electroencephalography-to-Text Decoding and Zero-Shot Sentiment Classification


  • Zhenhailong Wang University of Illinois at Urbana-Champaign
  • Heng Ji University of Illinois at Urbana-Champaign



Humans And AI (HAI), Speech & Natural Language Processing (SNLP)


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. The code is made publicly available for research purpose at




How to Cite

Wang, Z., & Ji, H. (2022). Open Vocabulary Electroencephalography-to-Text Decoding and Zero-Shot Sentiment Classification. Proceedings of the AAAI Conference on Artificial Intelligence, 36(5), 5350-5358.



AAAI Technical Track on Humans and AI