https://github.com/powerfulbean/nntrf
artificial neural network for modelling temporal responses
https://github.com/powerfulbean/nntrf
Last synced: 3 months ago
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artificial neural network for modelling temporal responses
- Host: GitHub
- URL: https://github.com/powerfulbean/nntrf
- Owner: powerfulbean
- License: mit
- Created: 2021-03-25T05:00:35.000Z (about 5 years ago)
- Default Branch: v1
- Last Pushed: 2025-10-30T20:15:13.000Z (7 months ago)
- Last Synced: 2026-01-03T03:56:04.265Z (5 months ago)
- Language: Python
- Homepage:
- Size: 16.5 MB
- Stars: 5
- Watchers: 1
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- License: LICENSE
- Citation: CITATION.cff
Awesome Lists containing this project
README
# nnTRF - neural network Temporal Response Function
This package is an artificial neural network implementation for temporal responses function modelling of brain signal. It implement the linear time-invariant TRF ([mTRF-Toolbox](https://github.com/mickcrosse/mTRF-Toolbox), [mTRFpy](https://github.com/powerfulbean/mTRFpy)), the [dynamic TRF](https://doi.org/10.1101/2024.08.26.609779) framework and more!
## Roadmap
🚧 In Progress | ✅ Completed | 🧪 Testing | 🔜 Planned | 📦 Released
🔜 self-developed fourier basis solver
## Installation
You can get the stable release from PyPI:
```sh
pip install nntrf
```
Or get the latest version from this repo:
```sh
pip install git+https://github.com/powerfulbean/nnTRF.git
```
## Citing nnTRF
Dou, J., Anderson, A. J., White, A. S., Norman-Haignere, S. V., & Lalor, E. C. (2024). Dynamic modeling of EEG responses to natural speech reveals earlier processing of predictable words. bioRxiv, 2024-08.
```
@article {Dou2024.08.26.609779,
author = {Dou, Jin and Anderson, Andrew J. and White, Aaron S. and Norman-Haignere, Samuel V. and Lalor, Edmund C.},
title = {Dynamic modeling of EEG responses to natural speech reveals earlier processing of predictable words},
elocation-id = {2024.08.26.609779},
year = {2024},
doi = {10.1101/2024.08.26.609779},
publisher = {Cold Spring Harbor Laboratory},
URL = {https://www.biorxiv.org/content/early/2024/08/26/2024.08.26.609779},
eprint = {https://www.biorxiv.org/content/early/2024/08/26/2024.08.26.609779.full.pdf},
journal = {bioRxiv}
}
```