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Architecture-only repository. Documents thebraindecode.models.EEGTCNetclass. No pretrained weights are distributed here. Instantiate the model and train it on your own data.
pip install braindecode1from braindecode.models import EEGTCNet
2
3model = EEGTCNet(
4 n_chans=22,
5 sfreq=250,
6 input_window_seconds=4.0,
7 n_outputs=4,
8)
| Parameter | Type | Description |
|---|---|---|
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. |
dropout | float, optional | Dropout rate. Default is 0.5. |
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. |
1@article{aristimunha2025braindecode,
2 title = {Braindecode: a deep learning library for raw electrophysiological data},
3 author = {Aristimunha, Bruno and others},
4 journal = {Zenodo},
5 year = {2025},
6 doi = {10.5281/zenodo.17699192},
7}