--- license: bsd-3-clause library_name: braindecode pipeline_tag: feature-extraction tags: - eeg - biosignal - pytorch - neuroscience - braindecode - convolutional --- # Deep4Net Deep ConvNet model from Schirrmeister et al (2017) . > **Architecture-only repository.** This repo documents the > `braindecode.models.Deep4Net` class. **No pretrained weights are > distributed here** — instantiate the model and train it on your own > data, or fine-tune from a published foundation-model checkpoint > separately. ## Quick start ```bash pip install braindecode ``` ```python from braindecode.models import Deep4Net model = Deep4Net( n_chans=22, sfreq=250, input_window_seconds=4.0, n_outputs=4, ) ``` The signal-shape arguments above are example defaults — adjust them to match your recording. ## Documentation - Full API reference (parameters, references, architecture figure): - Interactive browser with live instantiation: - Source on GitHub: ## Architecture description The block below is the rendered class docstring (parameters, references, architecture figure where available).

Deep ConvNet model from Schirrmeister et al (2017) [Schirrmeister2017]_.

Convolution .. figure:: https://onlinelibrary.wiley.com/cms/asset/fc200ccc-d8c4-45b4-8577-56ce4d15999a/hbm23730-fig-0001-m.jpg :align: center :alt: Deep4Net Architecture :width: 600px Model described in [Schirrmeister2017]_. Parameters ---------- final_conv_length: int | str Length of the final convolution layer. If set to "auto", n_times must not be None. Default: "auto". n_filters_time: int Number of temporal filters. n_filters_spat: int Number of spatial filters. filter_time_length: int Length of the temporal filter in layer 1. pool_time_length: int Length of temporal pooling filter. pool_time_stride: int Length of stride between temporal pooling filters. n_filters_2: int Number of temporal filters in layer 2. filter_length_2: int Length of the temporal filter in layer 2. n_filters_3: int Number of temporal filters in layer 3. filter_length_3: int Length of the temporal filter in layer 3. n_filters_4: int Number of temporal filters in layer 4. filter_length_4: int Length of the temporal filter in layer 4. activation_first_conv_nonlin: nn.Module, default is nn.ELU Non-linear activation function to be used after convolution in layer 1. first_pool_mode: str Pooling mode in layer 1. "max" or "mean". first_pool_nonlin: callable Non-linear activation function to be used after pooling in layer 1. activation_later_conv_nonlin: nn.Module, default is nn.ELU Non-linear activation function to be used after convolution in later layers. later_pool_mode: str Pooling mode in later layers. "max" or "mean". later_pool_nonlin: callable Non-linear activation function to be used after pooling in later layers. drop_prob: float Dropout probability. split_first_layer: bool Split first layer into temporal and spatial layers (True) or just use temporal (False). There would be no non-linearity between the split layers. batch_norm: bool Whether to use batch normalisation. batch_norm_alpha: float Momentum for BatchNorm2d. stride_before_pool: bool Stride before pooling. References ---------- .. [Schirrmeister2017] Schirrmeister, R. T., Springenberg, J. T., Fiederer, L. D. J., Glasstetter, M., Eggensperger, K., Tangermann, M., Hutter, F. & Ball, T. (2017). Deep learning with convolutional neural networks for EEG decoding and visualization. Human Brain Mapping , Aug. 2017. Online: http://dx.doi.org/10.1002/hbm.23730 .. rubric:: Hugging Face Hub integration When the optional ``huggingface_hub`` package is installed, all models automatically gain the ability to be pushed to and loaded from the Hugging Face Hub. Install with:: pip install braindecode[hub] **Pushing a model to the Hub:** .. code:: from braindecode.models import Deep4Net # Train your model model = Deep4Net(n_chans=22, n_outputs=4, n_times=1000) # ... training code ... # Push to the Hub model.push_to_hub( repo_id="username/my-deep4net-model", commit_message="Initial model upload", ) **Loading a model from the Hub:** .. code:: from braindecode.models import Deep4Net # Load pretrained model model = Deep4Net.from_pretrained("username/my-deep4net-model") # Load with a different number of outputs (head is rebuilt automatically) model = Deep4Net.from_pretrained("username/my-deep4net-model", n_outputs=4) **Extracting features and replacing the head:** .. code:: import torch x = torch.randn(1, model.n_chans, model.n_times) # Extract encoder features (consistent dict across all models) out = model(x, return_features=True) features = out["features"] # Replace the classification head model.reset_head(n_outputs=10) **Saving and restoring full configuration:** .. code:: import json config = model.get_config() # all __init__ params with open("config.json", "w") as f: json.dump(config, f) model2 = Deep4Net.from_config(config) # reconstruct (no weights) All model parameters (both EEG-specific and model-specific such as dropout rates, activation functions, number of filters) are automatically saved to the Hub and restored when loading. See :ref:`load-pretrained-models` for a complete tutorial.
## Citation Please cite both the original paper for this architecture (see the *References* section 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.