braindecode.models.EEGTCNet#

class braindecode.models.EEGTCNet(n_chans=None, n_outputs=None, n_times=None, chs_info=None, input_window_seconds=None, sfreq=None, activation=<class 'torch.nn.modules.activation.ELU'>, depth_multiplier=2, filter_1=8, kern_length=64, drop_prob=0.5, depth=2, kernel_size=4, filters=12, max_norm_const=0.25)[source]#

EEGTCNet model from Ingolfsson et al. (2020) [ingolfsson2020].

Convolution Recurrent

EEGTCNet Architecture

Combining EEGNet and TCN blocks.

Parameters:

  • n_chans (int) – Number of EEG channels.

  • n_outputs (int) – Number of outputs of the model. This is the number of classes in the case of classification.

  • n_times (int) – Number of time samples of the input window.

  • chs_info (list of dict) – Information about each individual EEG channel. This should be filled with info["chs"]. Refer to mne.Info for more details.

  • input_window_seconds (float) – Length of the input window in seconds.

  • sfreq (float) – Sampling frequency of the EEG recordings.

  • activation (nn.Module, optional) – Activation function to use. Default is nn.ELU().

  • depth_multiplier (int, optional) – Depth multiplier for the depthwise convolution. Default is 2.

  • filter_1 (int, optional) – Number of temporal filters in the first convolutional layer. Default is 8.

  • kern_length (int, optional) – Length of the temporal kernel in the first convolutional layer. Default is 64.

  • drop_prob (float) – The description is missing.

  • depth (int, optional) – Number of residual blocks in the TCN. Default is 2.

  • kernel_size (int, optional) – Size of the temporal convolutional kernel in the TCN. Default is 4.

  • filters (int, optional) – Number of filters in the TCN convolutional layers. Default is 12.

  • max_norm_const (float) – Maximum L2-norm constraint imposed on weights of the last fully-connected layer. Defaults to 0.25.

Raises:

ValueError – If some input signal-related parameters are not specified: and can not be inferred.

Notes

If some input signal-related parameters are not specified, there will be an attempt to infer them from the other parameters.

Hugging Face Hub integration

When the optional huggingface_hub package is installed, all models automatically gain the ability to be pushed to and loaded from the Hugging Face Hub. Install with:

pip install braindecode[hug]

Pushing a model to the Hub:

from braindecode.models import EEGNetv4

# Train your model
model = EEGNetv4(n_chans=22, n_outputs=4, n_times=1000)
# ... training code ...

# Push to the Hub
model.push_to_hub(
    repo_id="username/my-eegnet-model", commit_message="Initial model upload"
)

Loading a model from the Hub:

from braindecode.models import EEGNetv4

# Load pretrained model
model = EEGNetv4.from_pretrained("username/my-eegnet-model")

The integration automatically handles EEG-specific parameters (n_chans, n_times, sfreq, chs_info, etc.) by saving them in a config file alongside the model weights. This ensures that loaded models are correctly configured for their original data specifications.

Important

Currently, only EEG-specific parameters (n_outputs, n_chans, n_times, input_window_seconds, sfreq, chs_info) are saved to the Hub. Model-specific parameters (e.g., dropout rates, activation functions, number of filters) are not preserved and will use their default values when loading from the Hub.

To use non-default model parameters, specify them explicitly when calling from_pretrained():

model = EEGNet.from_pretrained("user/model", dropout=0.3, activation='relu')

Full parameter serialization will be addressed in a future update.

References

[ingolfsson2020]

Ingolfsson, T. M., Hersche, M., Wang, X., Kobayashi, N., Cavigelli, L., & Benini, L. (2020). EEG-TCNet: An accurate temporal convolutional network for embedded motor-imagery brain–machine interfaces. https://doi.org/10.48550/arXiv.2006.00622

Methods

forward(x)[source]#

Forward pass of the EEGTCNet model.

Parameters:

x (torch.Tensor) – Input tensor of shape (batch_size, n_chans, n_times).

Returns:

Output tensor of shape (batch_size, n_outputs).

Return type:

torch.Tensor