A bioelectric neural interface towards intuitive prosthetic control for amputees

Nguyen, Anh Tuan; Xu, Jian; Jiang, Ming; Luu, Diu Khue; Wu, Tong; Tam, Wing; Zhao, Wenfeng; Drealan, Markus W.; Overstreet, Cynthia K.; Zhao, Qi; Cheng, Jonathan; Keefer, Edward W.; Yang, Zhi

doi:10.1088/1741-2552/abc3d3

Cited by 36 publications

(30 citation statements)

References 63 publications

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“…The patient has four FAST-LIFE microelectrode arrays implanted in the residual ulnar and median nerve (Overstreet, 2019 ). Peripheral nerve signals are acquired by two Scorpius neural interface devices (Nguyen and Xu, 2020 ). The ground-truth movements are obtained with a data glove.…”

Section: Data Descriptionmentioning

confidence: 99%

“…It is achieved by decoding the subject's motor intent with neural data acquired from different parts of the nervous system. Proven approaches include surface electromyogram (EMG) (Sebelius, 2005 ; Fougner, 2012 ; Jiang, 2012 ; Amsuss, 2013 ; Zuleta, 2019 ; George, 2020a , b ), electroencephalogram (EEG) (Hu, 2015 ; Zeng, 2015 ; Sakhavi, 2018 ; Kwon, 2019 ), cortical recordings (Mollazadeh, 2011 ; Hochberg, 2012 ; Irwin, 2017 ), and peripheral nerve recordings (Micera, 2011 ; Davis, 2016 ; Vu, 2017 , 2020 ; Wendelken, 2017 ; Zhang, 2017 ; Nguyen and Xu, 2020 ).…”

Section: Introductionmentioning

confidence: 99%

“…In recent years, deep learning techniques have emerged as strong candidates to overcome this challenge thanks to their ability to process and analyze biological big data (Mahmud, 2018 ). Our previous work (Nguyen and Xu, 2020 ) shows that neural decoders based on the convolutional neural network (CNN) and recurrent neural network (RNN) architecture outperform other “classic” machine learning counterparts in decoding motor intents from peripheral nerve data obtained with an implantable bioelectric neural interface. The deep learning-based motor decoders can regress the intended motion of 15 degrees-of-freedom (DOF) simultaneously, including flexion/extension and abduction/adduction of individual fingers state-of-the-art performance metrics, thus complying with the dexterity and intuitiveness criteria.…”

Section: Introductionmentioning

confidence: 99%

“…Here we build upon the foundation of Nguyen and Xu ( 2020 ) by exploring different strategies to optimize the motor decoding paradigm's efficiency. The aim is to lower the neural decoder's computational complexity while retaining high accuracy predictions to make it feasible to translate the motor decoding paradigm to real-time operation suitable for clinical applications, especially when deploying in a portable platform.…”

Section: Introductionmentioning

confidence: 99%

“…First, we utilize feature extraction to reduce data dimensionality. By examining the data spectrogram, we learn that most of the signals' power concentrates in the frequency band 25–600 Hz (Nguyen and Xu, 2020 ). Many feature extraction techniques (Zardoshti-Kermani, 1995 ; Phinyomark, 2009 , 2012 ; Rafiee, 2011 ) have been developed to handle signals with similar characteristics.…”

Section: Introductionmentioning

confidence: 99%

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

et al. 2021

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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.

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Section: Data Descriptionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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

et al. 2021

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Implementation of surface electromyography controlled prosthetics limb based on recurrent neural network

Bittibssi

Zekry

Genedy

et al. 2022

Concurrency and Computation

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Low‐cost traditional prosthetic legs, available worldwide, can make walking and stair climbing possible but still difficult. This article presents the hardware implementation of the surface electromyography (sEMG) powered prosthesis actuation (PPA) system using a learned neural network algorithm based on recurrent neural network (RNN), which is used to train sEMG benchmark databases, and predict joint angle. This implementation was created based on sEMG signal measurements. The data were collected from three benchmark datasets describing different subjects during performance, and analyzing various gait patterns were used to construct the neural network and reduce significant model errors in a real‐time setting. Processing circuits, interfacing the output with the controller board, signal amplification, motor driving circuits, and single‐board computer programming are included in the implementation.

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Closed-Loop/Bidirectional Neuroprosthetic Systems

Gagnon-Turcotte

Tsiakaka

Bilodeau

et al. 2022

Handbook of Biochips

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A bioelectric neural interface towards intuitive prosthetic control for amputees

Cited by 36 publications

References 63 publications

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

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

Implementation of surface electromyography controlled prosthetics limb based on recurrent neural network

Closed-Loop/Bidirectional Neuroprosthetic Systems

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