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

May 10, 2019 · The Cartographer · 🏛 arXiv.org

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"Title-pattern auto-detect: A Survey on Deep Learning-based Non-Invasive Brain Signals:Recent Advances and New Frontiers"

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Authors Xiang Zhang, Lina Yao, Xianzhi Wang, Jessica Monaghan, David Mcalpine, Yu Zhang arXiv ID 1905.04149 Category cs.HC: Human-Computer Interaction Cross-listed cs.LG, eess.SP, q-bio.NC Citations 146 Venue arXiv.org Last Checked 8 days ago

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.