Human-computer Interaction for Brain-inspired Computing Based on Machine Learning And Deep Learning: A Review
CoRR(2023)
摘要
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 machine learning (ML) and deep learning (DL),
tracking their 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
human-computer interaction for brain-inspired computing. In addition, the
latest progress of deep learning in different tasks of human-computer
interaction for brain-inspired computing is reviewed from six perspectives,
such as data sets 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.
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