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Estimating muscle activation from EMG using deep learning-based dynamical systems models
Lahiru N Wimalasena1, Jonas F Braun2,3, Mohammad Reza Keshtkaran1
1Wallace H. Coulter Department of Biomedical Engineering, Emory University and Georgia Institute of Technology, Atlanta, GA, United States of America.
Journal of Neural Engineering
|April 2, 2022
Summary
This study introduces AutoLFADS, a deep learning method for estimating muscle activation from electromyographic (EMG) signals. The approach improves movement prediction and reveals new insights into neural control of movement.
Area of Science:
- Neuroscience
- Biomedical Engineering
- Machine Learning
Background:
- Estimating muscle activation from electromyographic (EMG) signals is crucial for understanding neural control of movement.
- Current methods for extracting neural command signals from EMG are limited, often requiring independent muscle analysis or manual hyperparameter tuning.
- The complex relationship between neural commands and recorded EMG signals presents a significant challenge in movement neuroscience.
Purpose of the Study:
- To adapt and apply AutoLFADS, a deep learning framework, for unsupervised estimation of multi-muscle activation from EMG recordings.
- To evaluate the performance of AutoLFADS in capturing dynamic muscle activation patterns during different behavioral tasks.
- To compare AutoLFADS-derived muscle activation estimates against traditional filtering methods and their correlation with neural activity.
Main Methods:
- Utilized AutoLFADS, an unsupervised deep learning approach employing recurrent neural networks, to model spatial and temporal patterns in multi-muscle EMG data.
- Applied the adapted AutoLFADS model to EMG recordings from rat hindlimb locomotion and monkey forearm isometric force tasks.
- Compared AutoLFADS performance against low-pass and Bayesian filtering for predicting joint kinematics and correlating with motor cortical activity.
Main Results:
- AutoLFADS dynamically adjusted its frequency response during rat locomotion, improving joint kinematics prediction compared to conventional filters.
- In monkey forearm tasks, AutoLFADS identified uncharacterized high-frequency EMG oscillations that correlated strongly with measured force.
- AutoLFADS-inferred muscle activation estimates showed higher correlation with simultaneously recorded motor cortical activity than other methods.
Conclusions:
- The adapted AutoLFADS method effectively estimates multi-muscle activation from EMG signals using dynamical systems and deep learning.
- This approach offers improved accuracy in muscle activation estimation, enhancing the prediction of movement kinematics and force.
- AutoLFADS provides a powerful tool for studying multi-muscle coordination, neural control, and advancing brain-machine interfaces.
