braindecode.models.BIOT#

class braindecode.models.BIOT(embed_dim=256, num_heads=8, num_layers=4, sfreq=200, hop_length=100, return_feature=False, n_outputs=None, n_chans=None, chs_info=None, n_times=None, input_window_seconds=None, activation=<class 'torch.nn.modules.activation.ELU'>, drop_prob=0.5, max_seq_len=1024, att_drop_prob=0.2, att_layer_drop_prob=0.2)[source]#

BIOT from Yang et al. (2023) [Yang2023]

Foundation Model

BioT

BIOT: Cross-data Biosignal Learning in the Wild.

BIOT is a foundation model for biosignal classification. It is a wrapper around the BIOTEncoder and ClassificationHead modules.

It is designed for N-dimensional biosignal data such as EEG, ECG, etc. The method was proposed by Yang et al. [Yang2023] and the code is available at [Code2023]

The model is trained with a contrastive loss on large EEG datasets TUH Abnormal EEG Corpus with 400K samples and Sleep Heart Health Study 5M. Here, we only provide the model architecture, not the pre-trained weights or contrastive loss training.

The architecture is based on the LinearAttentionTransformer and PatchFrequencyEmbedding modules. The BIOTEncoder is a transformer that takes the input data and outputs a fixed-size representation of the input data. More details are present in the BIOTEncoder class.

The ClassificationHead is an ELU activation layer, followed by a simple linear layer that takes the output of the BIOTEncoder and outputs the classification probabilities.

Important

Pre-trained Weights Available

This model has pre-trained weights available on the Hugging Face Hub. You can load them using:

from braindecode.models import BIOT

# Load the original pre-trained model from Hugging Face Hub
# For 16-channel models:
model = BIOT.from_pretrained("braindecode/biot-pretrained-prest-16chs")

# For 18-channel models:
model = BIOT.from_pretrained("braindecode/biot-pretrained-shhs-prest-18chs")
model = BIOT.from_pretrained("braindecode/biot-pretrained-six-datasets-18chs")

To push your own trained model to the Hub:

# After training your model
model.push_to_hub(
    repo_id="username/my-biot-model", commit_message="Upload trained BIOT model"
)

Requires installing braindecode[hug] for Hub integration.

Added in version 0.9.

Parameters:

  • embed_dim (int, optional) – The size of the embedding layer, by default 256

  • num_heads (int, optional) – The number of attention heads, by default 8

  • num_layers (int, optional) – The number of transformer layers, by default 4

  • activation (nn.Module, default=nn.ELU) – Activation function class to apply. Should be a PyTorch activation module class like nn.ReLU or nn.ELU. Default is nn.ELU.

  • return_feature (bool, optional) – Changing the output for the neural network. Default is single tensor when return_feature is True, return embedding space too. Default is False.

  • hop_length (int, optional) – The hop length for the torch.stft transformation in the encoder. The default is 100.

  • sfreq (int, optional) – The sfreq parameter for the encoder. The default is 200

References

[Yang2023] (1,2)

Yang, C., Westover, M.B. and Sun, J., 2023, November. BIOT: Biosignal Transformer for Cross-data Learning in the Wild. In Thirty-seventh Conference on Neural Information Processing Systems, NeurIPS.

[Code2023]

Yang, C., Westover, M.B. and Sun, J., 2023. BIOT Biosignal Transformer for Cross-data Learning in the Wild. GitHub ycq091044/BIOT (accessed 2024-02-13)

Methods

forward(x)[source]#

Pass the input through the BIOT encoder, and then through the classification head.

Parameters:

x (Tensor) – (batch_size, n_channels, n_times)

Returns:

  • out (Tensor) – (batch_size, n_outputs)

  • (out, emb) (tuple Tensor) – (batch_size, n_outputs), (batch_size, emb_size)

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