dpeeg provides a complete workflow for deep learning decoding EEG tasks, including basic datasets (datasets can be easily customized), basic network models, model training, rich experiments, and detailed experimental result storage.
Installation
- Create a new virtual environment named "dpeeg" with Python >= 3.10 using Anaconda3 and activate it：
conda create --name dpeeg python
conda activate dpeeg
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dpeeg depends on Pytorch. Please refer to the corresponding official website for installation.
-
Complete the installation via
pip:
pip install dpeeg
How to cite
If you would like to cite dpeeg you can do so using our Zenodo deposit.
1 maintainer
Metadata
1 maintainer
Release files for dpeeg 0.4.2
For a detailed explanation of source distributions (sdists) and built distributions (wheels), please see the package formats documentation.
Source distribution (sdist)
| File | Size | Uploaded | |
|---|---|---|---|
| dpeeg-0.4.2.tar.gz | 75.4 kB | Details |
Built distribution (wheel)
| File | Interpreter | ABI | Platform | Reset |
|---|---|---|---|---|
| dpeeg-0.4.2-py3-none-any.whl | Python 3 | none | any | Details |
Total release size: 173.1 kB
Release files / dpeeg-0.4.2.tar.gz
| Download URL | dpeeg-0.4.2.tar.gz |
|---|---|
| Size | 75.4 kB |
| Tags | Source |
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SHA-256 checksum How to use checksums |
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twine/4.0.2 CPython/3.10.14
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Release files / dpeeg-0.4.2-py3-none-any.whl
| Download URL | dpeeg-0.4.2-py3-none-any.whl |
|---|---|
| Size | 97.7 kB |
| Tags | Python 3 |
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twine/4.0.2 CPython/3.10.14
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