Estimating muscle activation from EMG using deep learning-based dynamical systems models.

ABSTRACT: Objective. To study the neural control of movement, it is often necessary to estimate how muscles are activated across a variety of behavioral conditions. One approach is to try extracting the underlying neural command signal to muscles by applying latent variable modeling methods to electromyographic (EMG) recordings. However, estimating the latent command signal that underlies muscle activation is challenging due to its complex relation with recorded EMG signals. Common approaches estimate each muscle's activation independently or require manual tuning of model hyperparameters to preserve behaviorally-relevant features.Approach. Here, we adapted AutoLFADS, a large-scale, unsupervised deep learning approach originally designed to de-noise cortical spiking data, to estimate m

SUBMITTER: Wimalasena LN 

PROVIDER: S-EPMC9628781 | biostudies-literature | 2022 May

REPOSITORIES: biostudies-literature