README.md

metadata

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

pip install braindecode

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

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:

@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.