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The continuous development of artificial intelligence has a profound impacton biomedicine and other fields, providing new research ideas and technicalmethods. Brain-inspired computing is an important intersection betweenmultimodal technology and biomedical field. Focusing on the applicationscenarios of decoding text and speech from brain signals in human-computerinteraction, this paper presents a comprehensive review of the brain-inspiredcomputing models based on deep learning (DL), tracking its evolution,application value, challenges and potential research trends. We first reviewsits basic concepts and development history, and divides its evolution into twostages: recent machine learning and current deep learning, emphasizing theimportance of each stage in the research of brain-inspired computing forhuman-computer interaction. In addition, the latest progress of deep learningin different tasks of brain-inspired computing for human-computer interactionis reviewed from five perspectives, including datasets and different brainsignals, and the application of key technologies in the model is elaborated indetail. Despite significant advances in brain-inspired computational models,challenges remain to fully exploit their capabilities, and we provide insightsinto possible directions for future academic research. For more detailedinformation, please visit our GitHub page:https://github.com/ultracoolHub/brain-inspired-computing.

Brain-inspired Computing Based on Deep Learning for Human-computer Interaction: A Review