AttnSleep

Sleep Staging Architecture from Eldele et al (2021) [Eldele2021].

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

model = AttnSleep(
    n_chans=2,
    sfreq=100,
    input_window_seconds=30.0,
    n_outputs=5,
)

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

Documentation

• Full API reference: https://braindecode.org/stable/generated/braindecode.models.AttnSleep.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/attn_sleep.py#L18

Architecture

Parameters

Parameter	Type	Description
n_tce	int	Number of TCE clones.
d_model	int	Input dimension for the TCE. Also the input dimension of the first FC layer in the feed forward and the output of the second FC layer in the same. Increase for higher sampling rate/signal length. It should be divisible by n_attn_heads
d_ff	int	Output dimension of the first FC layer in the feed forward and the input dimension of the second FC layer in the same.
n_attn_heads	int	Number of attention heads. It should be a factor of d_model
drop_prob	float	Dropout rate in the PositionWiseFeedforward layer and the TCE layers.
after_reduced_cnn_size	int	Number of output channels produced by the convolution in the AFR module.
return_feats	bool	If True, return the features, i.e. the output of the feature extractor (before the final linear layer). If False, pass the features through the final linear layer.
n_classes	int	Alias for n_outputs.
input_size_s	float	Alias for input_window_seconds.
activation	nn.Module, default=nn.ReLU	Activation function class to apply. Should be a PyTorch activation module class like nn.ReLU or nn.ELU. Default is nn.ReLU.
activation_mrcnn	nn.Module, default=nn.ReLU	Activation function class to apply in the Mask R-CNN layer. Should be a PyTorch activation module class like nn.ReLU or nn.GELU. Default is nn.GELU.

References

1. E. Eldele et al., "An Attention-Based Deep Learning Approach for Sleep Stage Classification With Single-Channel EEG," in IEEE Transactions on Neural Systems and Rehabilitation Engineering, vol. 29, pp. 809-818, 2021, doi: 10.1109/TNSRE.2021.3076234.
2. https://github.com/emadeldeen24/AttnSleep
3. https://sleepdata.org/datasets/shhs

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.