Initial deploy: tutorial index (notebooks pending make html)

README.md

@@ -0,0 +1,127 @@
+---
+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},
+}
+```