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

Xiang Zhang*, Lina Yao, Xianzhi Wang, Jessica Monaghan, David Mcalpine, Yu Zhang

*Corresponding author for this work

Research output: Contribution to journalReview articlepeer-review

3 Citations (Scopus)

Abstract

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.

Original languageEnglish
Article number031002
Pages (from-to)1-44
Number of pages44
JournalJournal of Neural Engineering
Volume18
Issue number3
DOIs
Publication statusPublished - Jun 2021

Keywords

  • brain-computer interface
  • deep learning algorithms
  • survey
  • brain signals

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