EEGConformer

EEG Conformer from Song et al (2022) [song2022].

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

model = EEGConformer(
    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.EEGConformer.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/eegconformer.py#L14

Architecture

Parameters

Parameter	Type	Description
n_filters_time: int	—	Number of temporal filters, defines also embedding size.
filter_time_length: int	—	Length of the temporal filter.
pool_time_length: int	—	Length of temporal pooling filter.
pool_time_stride: int	—	Length of stride between temporal pooling filters.
drop_prob: float	—	Dropout rate of the convolutional layer.
num_layers: int	—	Number of self-attention layers.
num_heads: int	—	Number of attention heads.
att_drop_prob: float	—	Dropout rate of the self-attention layer.
`final_fc_length: int	str`	—
return_features: bool	—	If True, the forward method returns the features before the last classification layer. Defaults to False.
activation: nn.Module	—	Activation function as parameter. Default is nn.ELU
activation_transfor: nn.Module	—	Activation function as parameter, applied at the FeedForwardBlock module inside the transformer. Default is nn.GeLU

References

1. Song, Y., Zheng, Q., Liu, B. and Gao, X., 2022. EEG conformer: Convolutional transformer for EEG decoding and visualization. IEEE Transactions on Neural Systems and Rehabilitation Engineering, 31, pp.710-719. https://ieeexplore.ieee.org/document/9991178
2. Song, Y., Zheng, Q., Liu, B. and Gao, X., 2022. EEG conformer: Convolutional transformer for EEG decoding and visualization. https://github.com/eeyhsong/EEG-Conformer.

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.