Brain-inspired Computing Based on Deep Learning for Human-computer Interaction: A Review
It addresses the problem of improving human-computer interaction for researchers and practitioners by synthesizing existing work, but it is incremental as a review paper.
This paper reviews brain-inspired computing models based on deep learning for decoding text and speech from brain signals in human-computer interaction, tracking their evolution, applications, and challenges.
The continuous development of artificial intelligence has a profound impact on biomedicine and other fields, providing new research ideas and technical methods. Brain-inspired computing is an important intersection between multimodal technology and biomedical field. Focusing on the application scenarios of decoding text and speech from brain signals in human-computer interaction, this paper presents a comprehensive review of the brain-inspired computing models based on deep learning (DL), tracking its evolution, application value, challenges and potential research trends. We first reviews its basic concepts and development history, and divides its evolution into two stages: recent machine learning and current deep learning, emphasizing the importance of each stage in the research of brain-inspired computing for human-computer interaction. In addition, the latest progress of deep learning in different tasks of brain-inspired computing for human-computer interaction is reviewed from five perspectives, including datasets and different brain signals, and the application of key technologies in the model is elaborated in detail. Despite significant advances in brain-inspired computational models, challenges remain to fully exploit their capabilities, and we provide insights into possible directions for future academic research. For more detailed information, please visit our GitHub page: https://github.com/ultracoolHub/brain-inspired-computing.