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

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

Research output: Contribution to journalArticlepeer-review

8 Scopus citations


Previous literature shows that deep learning is an effective tool to decode the motor intent from neural signals obtained from different parts of the nervous system. However, deep neural networks are often computationally complex and not feasible to work in real-time. Here we investigate different approaches' advantages and disadvantages to enhance the deep learning-based motor decoding paradigm's efficiency and inform its future implementation in real-time. Our data are recorded from the amputee's residual peripheral nerves. While the primary analysis is offline, the nerve data is cut using a sliding window to create a “pseudo-online” dataset that resembles the conditions in a real-time paradigm. First, a comprehensive collection of feature extraction techniques is applied to reduce the input data dimensionality, which later helps substantially lower the motor decoder's complexity, making it feasible for translation to a real-time paradigm. Next, we investigate two different strategies for deploying deep learning models: a one-step (1S) approach when big input data are available and a two-step (2S) when input data are limited. This research predicts five individual finger movements and four combinations of the fingers. The 1S approach using a recurrent neural network (RNN) to concurrently predict all fingers' trajectories generally gives better prediction results than all the machine learning algorithms that do the same task. This result reaffirms that deep learning is more advantageous than classic machine learning methods for handling a large dataset. However, when training on a smaller input data set in the 2S approach, which includes a classification stage to identify active fingers before predicting their trajectories, machine learning techniques offer a simpler implementation while ensuring comparably good decoding outcomes to the deep learning ones. In the classification step, either machine learning or deep learning models achieve the accuracy and F1 score of 0.99. Thanks to the classification step, in the regression step, both types of models result in a comparable mean squared error (MSE) and variance accounted for (VAF) scores as those of the 1S approach. Our study outlines the trade-offs to inform the future implementation of real-time, low-latency, and high accuracy deep learning-based motor decoder for clinical applications.

Original languageEnglish (US)
Article number667907
JournalFrontiers in Neuroscience
StatePublished - Jun 23 2021

Bibliographical note

Funding Information:
The remaining authors declare that this study received funding from Fasikl Inc. The funder had the following involvement with the study, including study design, data collection and analysis, and preparation of the manuscript.

Funding Information:
The surgery and patients related costs were supported in part by the Defense Advanced Research Projects Agency (DARPA) under Grants HR0011-17-2-0060 and N66001-15-C-4016. The technology development and human experiments were supported in part by internal funding from the University of Minnesota, in part by the NIH under Grant R21-NS111214, in part by NSF CAREER Award No. 1845709, and in part by Fasikl Incorporated.

Publisher Copyright:
© Copyright © 2021 Luu, Nguyen, Jiang, Xu, Drealan, Cheng, Keefer, Zhao and Yang.


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


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