Deep Learning-Based Approaches for Decoding Motor Intent From Peripheral Nerve Signals.

Luu, Diu K; Nguyen, Anh T; Jiang, Ming; Xu, Jian; Drealan, Markus W; Cheng, Jonathan; Keefer, Edward W; Zhao, Qi; Yang, Zhi

Luu, Diu K; Nguyen, Anh T; Jiang, Ming; Xu, Jian; Drealan, Markus W; Cheng, Jonathan; Keefer, Edward W; Zhao, Qi; Yang, Zhi.

Afiliación

Luu DK; Department of Biomedical Engineering, University of Minnesota, Minneapolis, MN, United States.
Nguyen AT; Department of Biomedical Engineering, University of Minnesota, Minneapolis, MN, United States.
Jiang M; Fasikl Incorporated, Minneapolis, MN, United States.
Xu J; Department of Computer Science and Engineering, University of Minnesota, Minneapolis, MN, United States.
Drealan MW; Department of Biomedical Engineering, University of Minnesota, Minneapolis, MN, United States.
Cheng J; Department of Biomedical Engineering, University of Minnesota, Minneapolis, MN, United States.
Keefer EW; Department of Plastic Surgery, University of Texas Southwestern Medical Center, Dallas, TX, United States.
Zhao Q; Nerves Incorporated, Dallas, TX, United States.
Yang Z; Nerves Incorporated, Dallas, TX, United States.

Front Neurosci ; 15: 667907, 2021.

Article en En | MEDLINE | ID: mdl-34248481

Palabras clave

convolutional neural network; deep learning; feature extraction; motor decoding; neural decoder; neuroprosthesis; peripheral nerve interface; recurrent neural network

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Texto completo: 1 Base de datos: MEDLINE Tipo de estudio: Prognostic_studies Idioma: En Revista: Front Neurosci Año: 2021 Tipo del documento: Article

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Texto completo: 1 Base de datos: MEDLINE Tipo de estudio: Prognostic_studies Idioma: En Revista: Front Neurosci Año: 2021 Tipo del documento: Article