Unsupervised neural decoding of signals recorded by thin-film electrode arrays implanted in muscles using autoencoding with a physiologically derived optimisation criterion
File(s)
Author(s)
Mayer, Kenneth MacSporran
Del Vecchio, Alessandro
Eskofier, Bjoern M
Farina, Dario
Type
Journal Article
Abstract
We present an autoencoder that learns the discharge times of individual motor units (MU) by processing
multichannel electromyogram (EMG) recordings obtained by electrode arrays implanted in muscles, thereby
providing a neural interface with the spinal cord. To this end, after preprocessing of the EMG via convolutive
sphering, the encoder is constrained to apply an orthogonal transformation to the spatial data and the
optimisation criterion enforces a temporal sparsity constraint. These constraints are based on the theoretical modelling of EMG generation. This decomposition method was evaluated on both simulated and experimental signals. For simulated signals, with a ratio of 1.5 between observations and sources, the average detection accuracy was 94% for up to 60 sources for SNR 20 dB. Moreover, the detection accuracy did not significantly decrease when decreasing the SNR to values as low as 0 dB. Experimental signals were collected in humans with thin-film invasive electrodes implanted in the tibialis anterior muscle. The results showed an accuracy on experimental data >90%. Moreover, the proposed method outperformed a state-of-the-art blind source separation approach in terms of the number of reliably detected motor units. In conclusion, we have proposed the first fully unsupervised neural network approach to the problem of neural decoding of intramuscular EMG time series by translating the available theoretical knowledge on the signal properties into an autoencoding architecture tailored to the decomposition problem.
multichannel electromyogram (EMG) recordings obtained by electrode arrays implanted in muscles, thereby
providing a neural interface with the spinal cord. To this end, after preprocessing of the EMG via convolutive
sphering, the encoder is constrained to apply an orthogonal transformation to the spatial data and the
optimisation criterion enforces a temporal sparsity constraint. These constraints are based on the theoretical modelling of EMG generation. This decomposition method was evaluated on both simulated and experimental signals. For simulated signals, with a ratio of 1.5 between observations and sources, the average detection accuracy was 94% for up to 60 sources for SNR 20 dB. Moreover, the detection accuracy did not significantly decrease when decreasing the SNR to values as low as 0 dB. Experimental signals were collected in humans with thin-film invasive electrodes implanted in the tibialis anterior muscle. The results showed an accuracy on experimental data >90%. Moreover, the proposed method outperformed a state-of-the-art blind source separation approach in terms of the number of reliably detected motor units. In conclusion, we have proposed the first fully unsupervised neural network approach to the problem of neural decoding of intramuscular EMG time series by translating the available theoretical knowledge on the signal properties into an autoencoding architecture tailored to the decomposition problem.
Date Issued
2023-09-01
Date Acceptance
2023-06-21
Citation
Biomedical Signal Processing and Control, 2023, 86 (Part B)
ISSN
1746-8094
Publisher
Elsevier
Journal / Book Title
Biomedical Signal Processing and Control
Volume
86
Issue
Part B
Copyright Statement
Copyright © Elsevier Ltd. All rights reserved. This manuscript version is made available under the CC-BY-NC-ND 4.0 license https://creativecommons.org/licenses/by-nc-nd/4.0/
Subjects
Autoencoder
Blind deconvolution
DECOMPOSITION
EMG signal decomposition
Engineering
Engineering, Biomedical
Science & Technology
Sparse signals
Technology
Unsupervised learning
Publication Status
Accepted
Article Number
105178
Date Publish Online
2023-07-07