Synthetic electroretinogram signal generation using a conditional generative adversarial network
The improved performance of the deep learning models with synthetic waveforms supports the application of AI to improve group classification with ERG recordings.
Authors
- Dorothy Thompson (ORCID: https://orcid.org/0000-0001-5491-3911)
- David Skuse (ORCID: https://orcid.org/0000-0002-7891-5732)
- Aleksei Zhdanov (ORCID: https://orcid.org/0000-0003-4725-3681)
- Paul A. Constable (ORCID: https://orcid.org/0000-0002-3994-1700)
- Andreas Maier (ORCID: https://orcid.org/0009-0006-7583-9144)
- Irene Lee (ORCID: https://orcid.org/0000-0003-0004-6802)
- Mikhail Kulyabin
Institutions
- Flinders University (AU)
- Friedrich-Alexander-Universität Erlangen-Nürnberg (DE)
- Great Ormond Street Hospital (GB)
- University College London (GB)
Publication Details
- Journal
- Documenta Ophthalmologica
- Published
- 2025-04-16
- DOI
- https://doi.org/10.1007/s10633-025-10019-0
- Citations
- 3
- Primary Topic
- EEG and Brain-Computer Interfaces
- Type
- article
- Field-Weighted Citation Impact
- 4.29
Funders
- Flinders University