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Architecture-only repository. Documents thebraindecode.models.SSTDPNclass. No pretrained weights are distributed here. Instantiate the model and train it on your own data.
pip install braindecode1from braindecode.models import SSTDPN
2
3model = SSTDPN(
4 n_chans=22,
5 sfreq=250,
6 input_window_seconds=4.0,
7 n_outputs=4,
8)
| Parameter | Type | Description |
|---|---|---|
n_spectral_filters_temporal | int, optional | Number of spectral filters extracted per channel via temporal convolution. These represent the temporal spectral bands (equivalent to :math:F_1 in the paper). Default is 9. |
n_fused_filters | int, optional | Number of output filters after pointwise fusion convolution. These fuse the spectral filters across all channels (equivalent to :math:F_2 in the paper). Default is 48. |
temporal_conv_kernel_size | int, optional | Kernel size for the temporal convolution layer. Controls the receptive field for extracting spectral information. Default is 75 samples. |
mvp_kernel_sizes | list[int], optional | Kernel sizes for Multi-scale Variance Pooling (MVP) module. Larger kernels capture long-term temporal dependencies . |
return_features | bool, optional | If True, the forward pass returns (features, logits). If False, returns only logits. Default is False. |
proto_sep_maxnorm | float, optional | Maximum L2 norm constraint for Inter-class Separation Prototypes during forward pass. This constraint acts as an implicit force to push features away from the origin. Default is 1.0. |
proto_cpt_std | float, optional | Standard deviation for Intra-class Compactness Prototype initialization. Default is 0.01. |
spt_attn_global_context_kernel | int, optional | Kernel size for global context embedding in Spatial-Spectral Attention module. Default is 250 samples. |
spt_attn_epsilon | float, optional | Small epsilon value for numerical stability in Spatial-Spectral Attention. Default is 1e-5. |
spt_attn_mode | str, optional | Embedding computation mode for Spatial-Spectral Attention ('var', 'l2', or 'l1'). Default is 'var' (variance-based mean-var operation). |
activation | nn.Module, optional | Activation function to apply after the pointwise fusion convolution in :class:_SSTEncoder. Should be a PyTorch activation module class. Default is nn.ELU. |
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}