---
license: bsd-3-clause
library_name: braindecode
pipeline_tag: feature-extraction
tags:
  - eeg
  - biosignal
  - pytorch
  - neuroscience
  - braindecode
  - convolutional
  - transformer
---

# SSTDPN

SSTDPN from Can Han et al (2025) [Han2025].

> **Architecture-only repository.** Documents the
> `braindecode.models.SSTDPN` class. **No pretrained weights are
> distributed here.** Instantiate the model and train it on your own
> data.

## Quick start

```bash
pip install braindecode
```

```python
from braindecode.models import SSTDPN

model = SSTDPN(
    n_chans=22,
    sfreq=250,
    input_window_seconds=4.0,
    n_outputs=4,
)
```

The signal-shape arguments above are illustrative defaults — adjust to
match your recording.

## Documentation
- Full API reference: <https://braindecode.org/stable/generated/braindecode.models.SSTDPN.html>
- Interactive browser (live instantiation, parameter counts):
  <https://huggingface.co/spaces/braindecode/model-explorer>
- Source on GitHub: <https://github.com/braindecode/braindecode/blob/master/braindecode/models/sstdpn.py#L17>


## Architecture

![SSTDPN architecture](https://raw.githubusercontent.com/hancan16/SST-DPN/refs/heads/main/figs/framework.png)


## Parameters

| 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. |


## References

1. Han, C., Liu, C., Wang, J., Wang, Y., Cai, C., & Qian, D. (2025). A spatial–spectral and temporal dual prototype network for motor imagery brain–computer interface. Knowledge-Based Systems, 315, 113315.
2. Han, C., Liu, C., Wang, J., Wang, Y., Cai, C., & Qian, D. (2025). A spatial–spectral and temporal dual prototype network for motor imagery brain–computer interface. Knowledge-Based Systems, 315, 113315. GitHub repository. https://github.com/hancan16/SST-DPN.


## Citation

Cite the original architecture paper (see *References* above) and braindecode:

```bibtex
@article{aristimunha2025braindecode,
  title   = {Braindecode: a deep learning library for raw electrophysiological data},
  author  = {Aristimunha, Bruno and others},
  journal = {Zenodo},
  year    = {2025},
  doi     = {10.5281/zenodo.17699192},
}
```

## License

BSD-3-Clause for the model code (matching braindecode).
Pretraining-derived weights, if you fine-tune from a checkpoint,
inherit the licence of that checkpoint and its training corpus.