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Fast Inference of Spinal Neuromodulation for Motor Control using Amortized Neural Networks

Epidural electrical stimulation (EES) has recently emerged as a potential therapeutic approach to restore motor function following chronic spinal cord injury (SCI). However, the lack of robust and systematic algorithms to automatically identify EES parameters that drive sensorimotor networks has become one of the main barriers to the clinical translation of EES. In this work, we present a novel, fully-automated computational framework to identify EES parameter combinations that optimally select for target muscle activation.

Creating a runtime environment

You can use the anaconda package manager to install the required dependencies

conda env create -f environment.yml
conda activate eesinference

Datasets

Data is available on request from the authors.

Forward model training

OMP_NUM_THREADS=1 python main.py hydra/launcher=joblib  \
                                 hydra.run.dir=<path to dir you want as a working directory> \
                                 model=mlp \
                                 mode=train \
                                 datamodule=sheep_20210610 \
                                 model_save_path=<path to save/load checkpoints> \
                                 model.network.out_size=7 \
                                 model.network.in_size=20 \
                                 trainer.device='cpu'

We note that model.network.out_size and model.network.in_size should be specified based on the number of EMG channels used and the EES parameter dimensionality respectively.

Forward model evaluation

To evaluate the forward model, one can use the same command as above but with the flag mode=eval. To extract error metrics (such as per-EMG channel L1 prediction errors) set mode=metrics. You can also perform a simple Maximum Likelikhood Estimation using a genetic algorithm to recover EES parameters by setting mode=MLE.

Training the electrode-conditioned inverse model

OMP_NUM_THREADS=1 python main.py  hydra/launcher=joblib  \
                                  hydra.run.dir=<path to dir you want as a working directory> \    
                                  model=mlp \
                                  mode=inference  \
                                  datamodule=sheep_20210610 \
                                  model_save_path=<path to save/load checkpoints> \
                                  electrode_index=<the electrode that you want to condition on. range [0, num_electrodes-1]>  \
                                  target_index=<pick a target EMG. Chosen from the test set>  \
                                  model.network.out_size=7 \
                                  model.network.in_size=20 \
                                  trainer.device='cpu'

Parallelize the training of electrode-conditioned inverse models

As mentioned in the manuscript, all the electrode-conditioned inverse models can be trained in parallel. We'll use the hydra joblib launcher for this purpose.

OMP_NUM_THREADS=1 python main.py  hydra/launcher=joblib  \
                                  hydra.run.dir=<path to dir you want as a working directory> \    
                                  model=mlp \
                                  mode=inference  \
                                  datamodule=sheep_20210610 \
                                  model_save_path=<path to save/load checkpoints> \
                                  electrode_index=0,1,2,3,...  \
                                  target_index=<pick a target EMG. Chosen from the test set. This can be parallelized too>  \
                                  model.network.out_size=7 \
                                  model.network.in_size=20 \
                                  trainer.device='cpu' \
                                  --multirun