Data underlying manuscript: Analysis of Power Losses and the Efficacy of Power Minimization Strategies in Multichannel Electrical Stimulation Systems

DOI:10.4121/b8098fe4-3f33-4691-9e55-54bf2cc255c3.v1
The DOI displayed above is for this specific version of this dataset, which is currently the latest. Newer versions may be published in the future. For a link that will always point to the latest version, please use
DOI: 10.4121/b8098fe4-3f33-4691-9e55-54bf2cc255c3

Datacite citation style

Varkevisser, Francesc; Serdijn, Wouter A.; Costa, Tiago L. (2025): Data underlying manuscript: Analysis of Power Losses and the Efficacy of Power Minimization Strategies in Multichannel Electrical Stimulation Systems. Version 1. 4TU.ResearchData. dataset. https://doi.org/10.4121/b8098fe4-3f33-4691-9e55-54bf2cc255c3.v1
Other citation styles (APA, Harvard, MLA, Vancouver, Chicago, IEEE) available at Datacite

Dataset

Data underlying the manuscript "Analysis of Power Losses and the Efficacy of Power Minimization Strategies in Multichannel Electrical Stimulation Systems". The manuscript proposes a methodology to analyze the efficacy of voltage scaling strategies in multichannel electrical stimulation systems.

This dataset contains the data and scripts used to produce the results presented in the manuscript.


'dataframes.pkl.gz' contains the data with the calculated power losses and efficiencies. Pickle format zipped with gzip.

'Power_loss_analysis.py' uses 'dataframes.pkl.gz' to produce the figures of the manuscript.

'Generate_dataframes.py' contains the functions used to calculate the associated power losses, allowing future use of the proposed methodology on different distributions.



History

  • 2025-01-14 first online, published, posted

Publisher

4TU.ResearchData

Format

*.pkl.gz, *.py

Funding

  • INTENSE (grant code 17619) NWO

Organizations

TU Delft, Faculty of Electrical Engineering, Mathematics and Computer Science, Department of Microelectronics, Section Bioelectronics

DATA

Files (4)