A survey on deep learning-based non-invasive brain signals: recent advances and new frontiers

Brain signals refer to the biometric information collected from the human brain. The research on brain signals aims to discover the underlying neurological or physical status of the individuals by signal decoding. The emerging deep learning techniques have improved the study of brain signals significantly in recent years. In this work, we first present a taxonomy of non-invasive brain signals and the basics of deep learning algorithms. Then, we provide the frontiers of applying deep learning for non-invasive brain signals analysis, by summarizing a large number of recent publications. Moreover, upon the deep learning-powered brain signal studies, we report the potential real-world applications which benefit not only disabled people but also normal individuals. Finally, we discuss the opening challenges and future directions.

Automated summary

Brain signals are biometric information collected from the human brain for decoding the underlying neurological or physical status of individuals. Recent advancements in deep learning have significantly enhanced the study of brain signals. This work presents a taxonomy of non-invasive brain signals, basics of deep learning algorithms, frontiers of applying deep learning for non-invasive brain signals analysis, and potential real-world applications.