Code for "Predicting the Temporal Dynamics of Prosthetic Vision" at IEEE EMBC 2024.
Please cite as:
Y Hou, L Pullela, J Su, S Aluru, S Sista, X Lu, M Beyeler (2024). Predicting the Temporal Dynamics of Prosthetic Vision. 46th Annual International Conference of the IEEE Engineering in Medicine & Biology Society (IEEE EMBC)
Note: Y Hou and L Pullela contribute equally to this work.
In this paper, we introduced two computational models designed to accurately predict phosphene fading and persistence under varying stimulus conditions, cross-validated on behavioral data reported by nine users of the Argus II Retinal Prosthesis System.
To run the notebooks, please install the required Python packages via pip:
pip install -r requirements.txt
baseline_training.ipynb
andbaseline_evaluation.ipynb
: baseline model training and evalution. Code adapted from Avraham, David, et al. “Retinal Prosthetic Vision Simulation: Temporal Aspects.” Journal of Neural Engineering, vol. 18, no. 4, IOP Publishing, Aug. 2021, p. 0460d9, doi:10.1088/1741-2552/ac1b6c.exponential_cross_subject.ipynb
: Exponential model trained and evaluted using leave-one-subject-out cross-validation.exponential_cross_stimulus.ipynb
: Exponential model trained and evaluted using leave-one-stimulus-condition-out cross-validation.FFT_cross_subject.ipynb
: FFT model trained and evaluted using leave-one-subject-out cross-validation.FFT_cross_stimulus.ipynb
: FFT model trained and evaluted using leave-one-stimulus-condition-out cross-validation.