Artificial Intelligence Enables Real-Time and Intuitive Control of Prostheses via Nerve Interface-Reference-Cited by-同舟云学术

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Artificial Intelligence Enables Real-Time and Intuitive Control of Prostheses via Nerve Interface

Published:2022-10 Issue:10 Volume:69 Page:3051-3063
ISSN:0018-9294
Container-title:IEEE Transactions on Biomedical Engineering
language:
Short-container-title:IEEE Trans. Biomed. Eng.

Author:

Luu Diu Khue¹^ORCID,Nguyen Anh Tuan¹^ORCID,Jiang Ming²^ORCID,Drealan Markus W.¹^ORCID,Xu Jian¹^ORCID,Wu Tong¹^ORCID,Tam Wing-kin¹,Zhao Wenfeng¹,Lim Brian Z. H.¹,Overstreet Cynthia K.³,Zhao Qi²,Cheng Jonathan⁴^ORCID,Keefer Edward W.³,Yang Zhi⁵

Affiliation:

1. Department of Biomedical Engineering, University of Minnesota, USA

2. Department of Computer Science and Engineering, University of Minnesota, USA

3. Nerves Incorporated, USA

4. Department of Plastic Surgery, University of Texas Southwestern Medical Center, USA

5. Department of Biomedical Engineering, University of Minnesota, Minneapolis, MN, USA

Funder

Defense Advanced Research Projects Agency

MnDRIVE Program and Institute for Engineering in Medicine

University of Minnesota

NIH

NSF CAREER

Fasikl Incorporated

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Subject

Biomedical Engineering

Link

https://ieeexplore.ieee.org/ielam/10/9895313/9738457-aam.pdf

Reference43 articles.

1. Dexterous Hand Control Through Fascicular Targeting (HAPTIX-DEFT)

2. Deep Learning Movement Intent Decoders Trained With Dataset Aggregation for Prosthetic Limb Control

3. A Deep Learning Framework for Decoding Motor Imagery Tasks of the Same Hand Using EEG Signals

4. Improved Training Paradigms and Motor-decode Algorithms: Results from Intact Individuals and a Recent Transradial Amputee with Prior Complex Regional Pain Syndrome

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