README.md

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

# Labram

Labram from Jiang, W B et al (2024) [Jiang2024].

> **Architecture-only repository.** Documents the
> `braindecode.models.Labram` 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 Labram

model = Labram(
    n_chans=22,
    sfreq=200,
    input_window_seconds=4.0,
    n_outputs=2,
)
```

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

## Documentation
- Full API reference: <https://braindecode.org/stable/generated/braindecode.models.Labram.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/labram.py#L196>


## Architecture

![Labram architecture](https://arxiv.org/html/2405.18765v1/x1.png)


## Parameters

| Parameter | Type | Description |
|---|---|---|
| `patch_size` | int | The size of the patch to be used in the patch embedding. |
| `learned_patcher` | bool | Whether to use a learned patch embedding (via a convolutional layer) or a fixed patch embedding (via rearrangement). |
| `embed_dim` | int | The dimension of the embedding. |
| `conv_in_channels` | int | The number of convolutional input channels. |
| `conv_out_channels` | int | The number of convolutional output channels. |
| `num_layers` |  int (default=12) | The number of attention layers of the model. |
| `num_heads` | int (default=10) | The number of attention heads. |
| `mlp_ratio` | float (default=4.0) | The expansion ratio of the mlp layer |
| `qkv_bias` |  bool (default=False) | If True, add a learnable bias to the query, key, and value tensors. |
| `qk_norm` | Pytorch Normalize layer (default=nn.LayerNorm) | If not None, apply LayerNorm to the query and key tensors. Default is nn.LayerNorm for better weight transfer from original LaBraM. Set to None to disable Q,K normalization. |
| `qk_scale` | float (default=None) | If not None, use this value as the scale factor. If None, use head_dim**-0.5, where head_dim = dim // num_heads. |
| `drop_prob` | float (default=0.0) | Dropout rate for the attention weights. |
| `attn_drop_prob` | float (default=0.0) | Dropout rate for the attention weights. |
| `drop_path_prob` | float (default=0.0) | Dropout rate for the attention weights used on DropPath. |
| `norm_layer` | Pytorch Normalize layer (default=nn.LayerNorm) | The normalization layer to be used. |
| `init_values` | float (default=0.1) | If not None, use this value to initialize the gamma_1 and gamma_2 parameters for residual scaling. Default is 0.1 for better weight transfer from original LaBraM. Set to None to disable. |
| `use_abs_pos_emb` | bool (default=True) | If True, use absolute position embedding. |
| `use_mean_pooling` | bool (default=True) | If True, use mean pooling. |
| `init_scale` | float (default=0.001) | The initial scale to be used in the parameters of the model. |
| `neural_tokenizer` | bool (default=True) | The model can be used in two modes: Neural Tokenizer or Neural Decoder. |
| `attn_head_dim` | bool (default=None) | The head dimension to be used in the attention layer, to be used only during pre-training. |
| `activation: nn.Module, default=nn.GELU` | — | Activation function class to apply. Should be a PyTorch activation module class like `nn.ReLU` or `nn.ELU`. Default is `nn.GELU`. |


## References

1. Wei-Bang Jiang, Li-Ming Zhao, Bao-Liang Lu. 2024, May. Large Brain Model for Learning Generic Representations with Tremendous EEG Data in BCI. The Twelfth International Conference on Learning Representations, ICLR.
2. Wei-Bang Jiang, Li-Ming Zhao, Bao-Liang Lu. 2024. Labram Large Brain Model for Learning Generic Representations with Tremendous EEG Data in BCI. GitHub https://github.com/935963004/LaBraM (accessed 2024-03-02)
3. Zhiliang Peng, Li Dong, Hangbo Bao, Qixiang Ye, Furu Wei. 2024. BEiT v2: Masked Image Modeling with Vector-Quantized Visual Tokenizers. arXiv:2208.06366 [cs.CV]


## 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.