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braindecode/EEGPT

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EEGPT

EEGPT: Pretrained Transformer for Universal and Reliable Representation of EEG Signals from Wang et al. (2024) [eegpt].

Architecture-only repository. Documents the braindecode.models.EEGPT 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 EEGPT

model = EEGPT(
    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.EEGPT.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/eegpt.py#L21>

Architecture

EEGPT architecture

Parameters

Parameter	Type	Description
`return_encoder_output`	bool, default=False	Whether to return the encoder output or the classifier output.
`patch_size`	int, default=64	Size of the patches for the transformer.
`patch_stride`	int, default=32	Stride of the patches for the transformer.
`embed_num`	int, default=4	Number of summary tokens used for the global representation.
`embed_dim`	int, default=512	Dimension of the embeddings.
`depth`	int, default=8	Number of transformer layers.
`num_heads`	int, default=8	Number of attention heads.
`mlp_ratio`	float, default=4.0	Ratio of the MLP hidden dimension to the embedding dimension.
`drop_prob`	float, default=0.0	Dropout probability.
`attn_drop_rate`	float, default=0.0	Attention dropout rate.
`drop_path_rate`	float, default=0.0	Drop path rate.
`init_std`	float, default=0.02	Standard deviation for weight initialization.
`qkv_bias`	bool, default=True	Whether to use bias in the QKV projection.
`norm_layer`	torch.nn.Module, default=None	Normalization layer. If None, defaults to `nn.LayerNorm` with epsilon `layer_norm_eps`.
`layer_norm_eps`	float, default=1e-6	Epsilon value for the normalization layer.

References

1.Wang, G., Liu, W., He, Y., Xu, C., Ma, L., & Li, H. (2024). EEGPT: Pretrained transformer for universal and reliable representation of eeg signals. Advances in Neural Information Processing Systems, 37, 39249-39280. Online: https://proceedings.neurips.cc/paper_files/paper/2024/file/4540d267eeec4e5dbd9dae9448f0b739-Paper-Conference.pdf

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.