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Architecture-only repository. Documents thebraindecode.models.EEGNeXclass. No pretrained weights are distributed here. Instantiate the model and train it on your own data.
pip install braindecode1from braindecode.models import EEGNeX
2
3model = EEGNeX(
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 filters in the first convolutional layer. Default is 8. |
filter_2 | int, optional | Number of filters in the second convolutional layer. Default is 32. |
drop_prob: float, optional | — | Dropout rate. Default is 0.5. |
kernel_block_4 | tuple[int, int], optional | Kernel size for block 4. Default is (1, 16). |
dilation_block_4 | tuple[int, int], optional | Dilation rate for block 4. Default is (1, 2). |
avg_pool_block4 | tuple[int, int], optional | Pooling size for block 4. Default is (1, 4). |
kernel_block_5 | tuple[int, int], optional | Kernel size for block 5. Default is (1, 16). |
dilation_block_5 | tuple[int, int], optional | Dilation rate for block 5. Default is (1, 4). |
avg_pool_block5 | tuple[int, int], optional | Pooling size for block 5. Default is (1, 8). |
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}