A survey on deep learning-based non-invasive brain signals: recent advances and new frontiers. (5th March 2021)

• Record Type: Journal Article
• Authors: Zhang, Xiang; Yao, Lina; Wang, Xianzhi; Monaghan, Jessica; McAlpine, David; Zhang, Yu
• Is Part Of: Journal of neural engineering. Volume 18:Number 3(2021)
• Publication Date: 2021-03-05
• Subjects: brain–computer interface -- deep learning algorithms -- survey -- brain signals; Neurosciences -- Periodicals; Biomedical engineering -- Periodicals; 612.8
• DOI: 10.1088/1741-2552/abc902
• Languages: English
• ISSNs: 1741-2560

Abstract

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.