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Architecture-only repository. Documents thebraindecode.models.EEGInceptionERPclass. No pretrained weights are distributed here. Instantiate the model and train it on your own data.
pip install braindecode1from braindecode.models import EEGInceptionERP
2
3model = EEGInceptionERP(
4 n_chans=22,
5 sfreq=250,
6 input_window_seconds=4.0,
7 n_outputs=4,
8)
| Parameter | Type | Description |
|---|---|---|
n_times | int, optional | Size of the input, in number of samples. Set to 128 (1s) as in [santamaria2020]. |
sfreq | float, optional | EEG sampling frequency. Defaults to 128 as in [santamaria2020]. |
drop_prob | float, optional | Dropout rate inside all the network. Defaults to 0.5 as in [santamaria2020]. |
scales_samples_s: list(float), optional | — | Windows for inception block. Temporal scale (s) of the convolutions on each Inception module. This parameter determines the kernel sizes of the filters. Defaults to 0.5, 0.25, 0.125 seconds, as in [santamaria2020]. |
n_filters | int, optional | Initial number of convolutional filters. Defaults to 8 as in [santamaria2020]. |
activation: nn.Module, optional | — | Activation function. Defaults to ELU activation as in [santamaria2020]. |
batch_norm_alpha: float, optional | — | Momentum for BatchNorm2d. Defaults to 0.01. |
depth_multiplier: int, optional | — | Depth multiplier for the depthwise convolution. Defaults to 2 as in [santamaria2020]. |
pooling_sizes: list(int), optional | — | Pooling sizes for the inception blocks. Defaults to 4, 2, 2 and 2, as in [santamaria2020]. |
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}