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

• Published in: Journal of neural engineering Vol. 18; no. 3; p. 31002
• Main Authors: Zhang, Xiang, Yao, Lina, Wang, Xianzhi, Monaghan, Jessica, McAlpine, David, Zhang, Yu
• Format: Journal Article
• Language: English
• Published: England 01.06.2021
• Subjects: Algorithms; Brain; Brain-Computer Interfaces; Deep Learning; Electroencephalography - methods; Humans; brain signals; brain–computer interface; survey; deep learning algorithms
• ISSN: 1741-2560; 1741-2552; 1741-2552
• DOI: 10.1088/1741-2552/abc902

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.