Why the study?
Emerging deep learning techniques have significantly improved the study of brain signals, warranting a summary of recent advances, applications, and challenges in analyzing non-invasive brain signals.
This comprehensive survey provides an overview of deep learning techniques applied to non-invasive brain signals, offering guidelines for model selection and highlighting future research frontiers.
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Guides DL model selection for non-invasive brain signals; leaves open prospective clinical validation.
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.
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Zhang et al. (2020) studied this question.
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