braindecode.models.TIDNet#

class braindecode.models.TIDNet(n_chans=None, n_outputs=None, n_times=None, input_window_seconds=None, sfreq=None, chs_info=None, s_growth=24, t_filters=32, drop_prob=0.4, pooling=15, temp_layers=2, spat_layers=2, temp_span=0.05, bottleneck=3, summary=-1, activation=<class 'torch.nn.modules.activation.LeakyReLU'>)[source]#

Thinker Invariance DenseNet model from Kostas et al (2020) [TIDNet].

Convolution

TIDNet Architecture

See [TIDNet] for details.

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.

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

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

  • 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.

  • s_growth (int) – DenseNet-style growth factor (added filters per DenseFilter)

  • t_filters (int) – Number of temporal filters.

  • drop_prob (float) – Dropout probability

  • pooling (int) – Max temporal pooling (width and stride)

  • temp_layers (int) – Number of temporal layers

  • spat_layers (int) – Number of DenseFilters

  • temp_span (float) – Percentage of n_times that defines the temporal filter length: temp_len = ceil(temp_span * n_times) e.g A value of 0.05 for temp_span with 1500 n_times will yield a temporal filter of length 75.

  • bottleneck (int) – Bottleneck factor within Densefilter

  • summary (int) – Output size of AdaptiveAvgPool1D layer. If set to -1, value will be calculated automatically (n_times // pooling).

  • activation (type[Module]) – Activation function class to apply. Should be a PyTorch activation module class like nn.ReLU or nn.ELU. Default is nn.LeakyReLU.

Raises:

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

Notes

Code adapted from: https://github.com/SPOClab-ca/ThinkerInvariance/

References

[TIDNet] (1,2)

Kostas, D. & Rudzicz, F. Thinker invariance: enabling deep neural networks for BCI across more people. J. Neural Eng. 17, 056008 (2020). doi: 10.1088/1741-2552/abb7a7.

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[hub]

Pushing a model to the Hub:

from braindecode.models import TIDNet

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

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

Loading a model from the Hub:

from braindecode.models import TIDNet

# Load pretrained model
model = TIDNet.from_pretrained("username/my-tidnet-model")

# Load with a different number of outputs (head is rebuilt automatically)
model = TIDNet.from_pretrained("username/my-tidnet-model", n_outputs=4)

Extracting features and replacing the head:

import torch

x = torch.randn(1, model.n_chans, model.n_times)
# Extract encoder features (consistent dict across all models)
out = model(x, return_features=True)
features = out["features"]

# Replace the classification head
model.reset_head(n_outputs=10)

Saving and restoring full configuration:

import json

config = model.get_config()            # all __init__ params
with open("config.json", "w") as f:
    json.dump(config, f)

model2 = TIDNet.from_config(config)    # reconstruct (no weights)

All model parameters (both EEG-specific and model-specific such as dropout rates, activation functions, number of filters) are automatically saved to the Hub and restored when loading.

See Loading and Adapting Pretrained Foundation Models for a complete tutorial.

Methods

forward(x)[source]#

Forward pass.

Parameters:

x (Tensor) – Batch of EEG windows of shape (batch_size, n_channels, n_times).

Return type:

Tensor

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