README.md

---
license: bsd-3-clause
language:
  - en
tags:
  - eeg
  - biosignal
  - tutorials
  - notebooks
  - braindecode
  - neuroscience
  - brain-computer-interface
pretty_name: Braindecode Tutorials
size_categories:
  - n<1K
---

# Braindecode Tutorials

Curated collection of **31 executable Jupyter notebooks** showing how to use
[braindecode](https://braindecode.org) for end-to-end EEG / biosignal deep
learning: data loading, preprocessing, model training, fine-tuning of
foundation models, and Hugging Face Hub integration.

> Each notebook is mirrored from the
> [`examples/`](https://github.com/braindecode/braindecode/tree/master/examples)
> directory of the upstream repo. They are auto-converted from the
> sphinx-gallery `.py` source on every release; the canonical rendered
> versions live at <https://braindecode.org/stable/auto_examples>.

## How to use this dataset repo

Three options, in increasing order of convenience:

| Option | What you do |
|---|---|
| **Open in Colab** | Click any `▶ Colab` link below. Free GPU runtime; no install. |
| **Download** | `huggingface-cli download braindecode/tutorials --repo-type dataset --local-dir tutorials/` |
| **Browse rendered HTML** | <https://braindecode.org/stable/auto_examples> (sphinx-gallery output). |

Each notebook installs braindecode in the first cell, so it's runnable
on a fresh kernel.

## Catalogue

### Getting started — datasets & I/O

| Notebook | Topic | Run |
|---|---|---|
| `plot_moabb_dataset_example.ipynb` | Load a MOABB dataset (BNCI 2014-001) | [▶ Colab](https://colab.research.google.com/github/braindecode/braindecode/blob/master/examples/datasets_io/plot_moabb_dataset_example.ipynb) |
| `plot_mne_dataset_example.ipynb` | Wrap an `mne.io.Raw` as a braindecode dataset | [▶ Colab](https://colab.research.google.com/github/braindecode/braindecode/blob/master/examples/datasets_io/plot_mne_dataset_example.ipynb) |
| `plot_bids_dataset_example.ipynb` | Load a BIDS-formatted EEG dataset | [▶ Colab](https://colab.research.google.com/github/braindecode/braindecode/blob/master/examples/datasets_io/plot_bids_dataset_example.ipynb) |
| `plot_custom_dataset_example.ipynb` | Roll your own dataset class | [▶ Colab](https://colab.research.google.com/github/braindecode/braindecode/blob/master/examples/datasets_io/plot_custom_dataset_example.ipynb) |
| `plot_load_save_datasets.ipynb` | Cache datasets to disk and reload them | [▶ Colab](https://colab.research.google.com/github/braindecode/braindecode/blob/master/examples/datasets_io/plot_load_save_datasets.ipynb) |
| `plot_split_dataset.ipynb` | Train/valid/test splits at session and subject level | [▶ Colab](https://colab.research.google.com/github/braindecode/braindecode/blob/master/examples/datasets_io/plot_split_dataset.ipynb) |
| `plot_benchmark_preprocessing.ipynb` | Compare preprocessing strategies head-to-head | [▶ Colab](https://colab.research.google.com/github/braindecode/braindecode/blob/master/examples/datasets_io/plot_benchmark_preprocessing.ipynb) |
| `plot_tuh_discrete_multitarget.ipynb` | TUH multi-target discrete labels | [▶ Colab](https://colab.research.google.com/github/braindecode/braindecode/blob/master/examples/datasets_io/plot_tuh_discrete_multitarget.ipynb) |
| `plot_hub_integration.ipynb` | Push / pull braindecode datasets to the Hub | [▶ Colab](https://colab.research.google.com/github/braindecode/braindecode/blob/master/examples/datasets_io/plot_hub_integration.ipynb) |

### Model building & training

| Notebook | Topic | Run |
|---|---|---|
| `plot_basic_training_epochs.ipynb` | Train any braindecode model in a few cells | [▶ Colab](https://colab.research.google.com/github/braindecode/braindecode/blob/master/examples/model_building/plot_basic_training_epochs.ipynb) |
| `plot_train_in_pure_pytorch_and_pytorch_lightning.ipynb` | Pure-PyTorch and Lightning training loops | [▶ Colab](https://colab.research.google.com/github/braindecode/braindecode/blob/master/examples/model_building/plot_train_in_pure_pytorch_and_pytorch_lightning.ipynb) |
| `plot_bcic_iv_2a_moabb_trial.ipynb` | BCI Competition IV 2a — trial-wise decoding | [▶ Colab](https://colab.research.google.com/github/braindecode/braindecode/blob/master/examples/model_building/plot_bcic_iv_2a_moabb_trial.ipynb) |
| `plot_bcic_iv_2a_moabb_cropped.ipynb` | BCI Competition IV 2a — cropped decoding | [▶ Colab](https://colab.research.google.com/github/braindecode/braindecode/blob/master/examples/model_building/plot_bcic_iv_2a_moabb_cropped.ipynb) |
| `plot_bcic_iv_2a_eegprep_cleaning.ipynb` | EEG-Prep cleaning before training | [▶ Colab](https://colab.research.google.com/github/braindecode/braindecode/blob/master/examples/model_building/plot_bcic_iv_2a_eegprep_cleaning.ipynb) |
| `plot_load_pretrained_models.ipynb` | **Load BENDR / BIOT / EEGPT from the Hub and fine-tune** | [▶ Colab](https://colab.research.google.com/github/braindecode/braindecode/blob/master/examples/model_building/plot_load_pretrained_models.ipynb) |
| `plot_how_train_test_and_tune.ipynb` | Cross-validation and hyper-parameter tuning | [▶ Colab](https://colab.research.google.com/github/braindecode/braindecode/blob/master/examples/model_building/plot_how_train_test_and_tune.ipynb) |
| `plot_hyperparameter_tuning_with_scikit-learn.ipynb` | scikit-learn `GridSearchCV` over braindecode | [▶ Colab](https://colab.research.google.com/github/braindecode/braindecode/blob/master/examples/model_building/plot_hyperparameter_tuning_with_scikit-learn.ipynb) |
| `plot_preprocessing_classes.ipynb` | The `Preprocessor` API | [▶ Colab](https://colab.research.google.com/github/braindecode/braindecode/blob/master/examples/model_building/plot_preprocessing_classes.ipynb) |
| `plot_channel_interpolation.ipynb` | Bad-channel interpolation | [▶ Colab](https://colab.research.google.com/github/braindecode/braindecode/blob/master/examples/model_building/plot_channel_interpolation.ipynb) |
| `plot_regression.ipynb` | EEG regression instead of classification | [▶ Colab](https://colab.research.google.com/github/braindecode/braindecode/blob/master/examples/model_building/plot_regression.ipynb) |

### Advanced training

| Notebook | Topic | Run |
|---|---|---|
| `plot_data_augmentation.ipynb` | EEG-specific augmentations | [▶ Colab](https://colab.research.google.com/github/braindecode/braindecode/blob/master/examples/advanced_training/plot_data_augmentation.ipynb) |
| `plot_data_augmentation_search.ipynb` | Search over augmentation policies | [▶ Colab](https://colab.research.google.com/github/braindecode/braindecode/blob/master/examples/advanced_training/plot_data_augmentation_search.ipynb) |
| `plot_relative_positioning.ipynb` | Self-supervised pretext tasks | [▶ Colab](https://colab.research.google.com/github/braindecode/braindecode/blob/master/examples/advanced_training/plot_relative_positioning.ipynb) |
| `plot_temporal_generalization.ipynb` | Temporal-generalisation matrices | [▶ Colab](https://colab.research.google.com/github/braindecode/braindecode/blob/master/examples/advanced_training/plot_temporal_generalization.ipynb) |
| `plot_finetune_foundation_model.ipynb` | Fine-tune a Hub foundation model end-to-end | [▶ Colab](https://colab.research.google.com/github/braindecode/braindecode/blob/master/examples/advanced_training/plot_finetune_foundation_model.ipynb) |
| `plot_moabb_benchmark.ipynb` | Run a full MOABB benchmark | [▶ Colab](https://colab.research.google.com/github/braindecode/braindecode/blob/master/examples/advanced_training/plot_moabb_benchmark.ipynb) |
| `plot_exca_config.ipynb` | Reproducible experiments with `exca` | [▶ Colab](https://colab.research.google.com/github/braindecode/braindecode/blob/master/examples/advanced_training/plot_exca_config.ipynb) |

### Applied examples

| Notebook | Topic | Run |
|---|---|---|
| `plot_sleep_staging_chambon2018.ipynb` | Sleep staging with Chambon2018 | [▶ Colab](https://colab.research.google.com/github/braindecode/braindecode/blob/master/examples/applied_examples/plot_sleep_staging_chambon2018.ipynb) |
| `plot_sleep_staging_usleep.ipynb` | Sleep staging with U-Sleep | [▶ Colab](https://colab.research.google.com/github/braindecode/braindecode/blob/master/examples/applied_examples/plot_sleep_staging_usleep.ipynb) |
| `plot_sleep_staging_eldele2021.ipynb` | Sleep staging with Eldele2021 | [▶ Colab](https://colab.research.google.com/github/braindecode/braindecode/blob/master/examples/applied_examples/plot_sleep_staging_eldele2021.ipynb) |
| `plot_tuh_eeg_corpus.ipynb` | Working with the TUH EEG corpus | [▶ Colab](https://colab.research.google.com/github/braindecode/braindecode/blob/master/examples/applied_examples/plot_tuh_eeg_corpus.ipynb) |

## How the notebooks are produced

Sphinx-gallery automatically generates a `.ipynb` next to each rendered
HTML page during the docs build. The conversion script for this
dataset repo simply collects those generated notebooks:

```bash
# in the braindecode repo
cd docs && make html         # builds HTML + .ipynb files
python ../hf_assets/tutorials_index/build_notebooks.py  # gathers .ipynb
```

See `build_notebooks.py` for the gather + push logic.

## Cost note

Hosting `.ipynb` files in a public dataset repo is **free** on Hugging
Face. Compute (Colab) is also free for the small datasets used in these
tutorials. No paid tier is required.

## Citation

```bibtex
@article{aristimunha2025braindecode,
  title   = {Braindecode: a deep learning library for raw electrophysiological data},
  author  = {Aristimunha, Bruno and others},
  journal = {Zenodo},
  year    = {2025},
  doi     = {10.5281/zenodo.17699192},
}
```