Brain-inspired Computing Based on Deep Learning for Human-computer Interaction: A Review
Bihui Yu, Sibo Zhang, Lili Zhou, Jingxuan Wei, Linzhuang Sun, Liping Bu

TL;DR
This review paper discusses the evolution, current state, and future challenges of brain-inspired computing models based on deep learning, focusing on human-computer interaction through decoding brain signals like text and speech.
Contribution
It provides a comprehensive overview of the development, recent progress, and research trends in brain-inspired deep learning models for HCI, highlighting key technological advancements and challenges.
Findings
Deep learning has significantly advanced brain-inspired computing models.
Progress has been made in decoding brain signals for HCI applications.
Challenges include fully exploiting model capabilities and integrating multimodal data.
Abstract
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…
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Taxonomy
TopicsEEG and Brain-Computer Interfaces · Advanced Memory and Neural Computing · Ferroelectric and Negative Capacitance Devices
