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A Survey on Deep Learning-based Non-Invasive Brain Signals:Recent Advances and New Frontiers (1905.04149v5)

Published 10 May 2019 in cs.HC, cs.LG, eess.SP, and q-bio.NC

Abstract: Brain-Computer Interface (BCI) bridges the human's neural world and the outer physical world by decoding individuals' brain signals into commands recognizable by computer devices. Deep learning has lifted the performance of brain-computer interface systems significantly in recent years. In this article, we systematically investigate brain signal types for BCI and related deep learning concepts for brain signal analysis. We then present a comprehensive survey of deep learning techniques used for BCI, by summarizing over 230 contributions most published in the past five years. Finally, we discuss the applied areas, opening challenges, and future directions for deep learning-based BCI.

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Authors (6)
  1. Xiang Zhang (395 papers)
  2. Lina Yao (194 papers)
  3. Xianzhi Wang (49 papers)
  4. Jessica Monaghan (1 paper)
  5. Yu Zhang (1400 papers)
  6. David McAlpine (5 papers)
Citations (135)