README.md

---
license: bsd-3-clause
library_name: braindecode
pipeline_tag: feature-extraction
tags:
  - eeg
  - biosignal
  - pytorch
  - neuroscience
  - braindecode
  - foundation-model
  - transformer
---

# REVE

**R**\ epresentation for **E**\ EG with **V**\ ersatile **E**\ mbeddings (REVE) from El Ouahidi et al. (2025) [reve].

> **Architecture-only repository.** Documents the
> `braindecode.models.REVE` class. **No pretrained weights are
> distributed here.** Instantiate the model and train it on your own
> data.

## Quick start

```bash
pip install braindecode
```

```python
from braindecode.models import REVE

model = REVE(
    n_chans=22,
    sfreq=250,
    input_window_seconds=4.0,
    n_outputs=4,
)
```

The signal-shape arguments above are illustrative defaults — adjust to
match your recording.

## Documentation
- Full API reference: <https://braindecode.org/stable/generated/braindecode.models.REVE.html>
- Interactive browser (live instantiation, parameter counts):
  <https://huggingface.co/spaces/braindecode/model-explorer>
- Source on GitHub: <https://github.com/braindecode/braindecode/blob/master/braindecode/models/reve.py#L35>


## Architecture

![REVE architecture](https://brain-bzh.github.io/reve/static/images/architecture.png)


## Parameters

| Parameter | Type | Description |
|---|---|---|
| `embed_dim` | int, default=512 | Embedding dimension. Use 512 for REVE-Base, 1250 for REVE-Large. |
| `depth` | int, default=22 | Number of Transformer layers. |
| `heads` | int, default=8 | Number of attention heads. |
| `head_dim` | int, default=64 | Dimension per attention head. |
| `mlp_dim_ratio` | float, default=2.66 | FFN hidden dimension ratio: `mlp_dim = embed_dim × mlp_dim_ratio`. |
| `use_geglu` | bool, default=True | Use GEGLU activation (recommended) or standard GELU. |
| `freqs` | int, default=4 | Number of frequencies for Fourier positional embedding. |
| `patch_size` | int, default=200 | Temporal patch size in samples (200 samples = 1 second at 200 Hz). |
| `patch_overlap` | int, default=20 | Overlap between patches in samples. |
| `attention_pooling` | bool, default=False | Pooling strategy for aggregating transformer outputs before classification. If `False` (default), all tokens are flattened into a single vector of size `(n_chans x n_patches x embed_dim)`, which is then passed through LayerNorm and a linear classifier. If `True`, uses attention-based pooling with a learnable query token that attends to all encoder outputs, producing a single embedding of size `embed_dim`. Attention pooling is more parameter-efficient for long sequences and variable-length inputs. |


## References

1. El Ouahidi, Y., Lys, J., Thölke, P., Farrugia, N., Pasdeloup, B., Gripon, V., Jerbi, K. & Lioi, G. (2025). REVE: A Foundation Model for EEG - Adapting to Any Setup with Large-Scale Pretraining on 25,000 Subjects. The Thirty-Ninth Annual Conference on Neural Information Processing Systems. https://openreview.net/forum?id=ZeFMtRBy4Z
2. Défossez, A., Caucheteux, C., Rapin, J., Kabeli, O., & King, J. R. (2023). Decoding speech perception from non-invasive brain recordings. Nature Machine Intelligence, 5(10), 1097-1107.


## Citation

Cite the original architecture paper (see *References* above) and braindecode:

```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},
}
```

## License

BSD-3-Clause for the model code (matching braindecode).
Pretraining-derived weights, if you fine-tune from a checkpoint,
inherit the licence of that checkpoint and its training corpus.