A survey on deep learning-based non-invasive brain signals: recent advances and new frontiers
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 signifi...
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| Vydané v: | Journal of neural engineering Ročník 18; číslo 3 |
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| Hlavní autori: | , , , , , |
| Médium: | Journal Article |
| Jazyk: | English |
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England
01.06.2021
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| ISSN: | 1741-2552, 1741-2552 |
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| 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. |
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| AbstractList | 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.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. 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. |
| Author | McAlpine, David Wang, Xianzhi Zhang, Yu Zhang, Xiang Yao, Lina Monaghan, Jessica |
| Author_xml | – sequence: 1 givenname: Xiang orcidid: 0000-0001-5097-2113 surname: Zhang fullname: Zhang, Xiang organization: Harvard University, Boston, Massachusetts, United States of America – sequence: 2 givenname: Lina surname: Yao fullname: Yao, Lina organization: University of New South Wales, Sydney, Australia – sequence: 3 givenname: Xianzhi surname: Wang fullname: Wang, Xianzhi organization: University of Technology Sydney, Sydney, Australia – sequence: 4 givenname: Jessica orcidid: 0000-0003-1416-4164 surname: Monaghan fullname: Monaghan, Jessica organization: Macquarie University, Sydney, Australia – sequence: 5 givenname: David surname: McAlpine fullname: McAlpine, David organization: Macquarie University, Sydney, Australia – sequence: 6 givenname: Yu surname: Zhang fullname: Zhang, Yu organization: Lehigh University, Bethlehem, Pennsylvania, United States of America |
| BackLink | https://www.ncbi.nlm.nih.gov/pubmed/33171452$$D View this record in MEDLINE/PubMed |
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| Title | A survey on deep learning-based non-invasive brain signals: recent advances and new frontiers |
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