| --- |
| license: bsd-3-clause |
| library_name: braindecode |
| pipeline_tag: feature-extraction |
| tags: |
| - eeg |
| - biosignal |
| - pytorch |
| - neuroscience |
| - braindecode |
| - convolutional |
| - transformer |
| --- |
| |
| # AttentionBaseNet |
|
|
| AttentionBaseNet from Wimpff M et al (2023) [Martin2023]. |
|
|
| > **Architecture-only repository.** Documents the |
| > `braindecode.models.AttentionBaseNet` 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 AttentionBaseNet |
| |
| model = AttentionBaseNet( |
| 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.AttentionBaseNet.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/attentionbasenet.py#L29> |
|
|
|
|
| ## Architecture |
|
|
|  |
|
|
|
|
| ## Parameters |
|
|
| | Parameter | Type | Description | |
| |---|---|---| |
| | `n_temporal_filters` | int, optional | Number of temporal convolutional filters in the first layer. This defines the number of output channels after the temporal convolution. Default is 40. | |
| | `temp_filter_length` | int, default=15 | The length of the temporal filters in the convolutional layers. | |
| | `spatial_expansion` | int, optional | Multiplicative factor to expand the spatial dimensions. Used to increase the capacity of the model by expanding spatial features. Default is 1. | |
| | `pool_length_inp` | int, optional | Length of the pooling window in the input layer. Determines how much temporal information is aggregated during pooling. Default is 75. | |
| | `pool_stride_inp` | int, optional | Stride of the pooling operation in the input layer. Controls the downsampling factor in the temporal dimension. Default is 15. | |
| | `drop_prob_inp` | float, optional | Dropout rate applied after the input layer. This is the probability of zeroing out elements during training to prevent overfitting. Default is 0.5. | |
| | `ch_dim` | int, optional | Number of channels in the subsequent convolutional layers. This controls the depth of the network after the initial layer. Default is 16. | |
| | `attention_mode` | str, optional | The type of attention mechanism to apply. If `None`, no attention is applied. - "se" for Squeeze-and-excitation network - "gsop" for Global Second-Order Pooling - "fca" for Frequency Channel Attention Network - "encnet" for context encoding module - "eca" for Efficient channel attention for deep convolutional neural networks - "ge" for Gather-Excite - "gct" for Gated Channel Transformation - "srm" for Style-based Recalibration Module - "cbam" for Convolutional Block Attention Module - "cat" for Learning to collaborate channel and temporal attention from multi-information fusion - "catlite" for Learning to collaborate channel attention from multi-information fusion (lite version, cat w/o temporal attention) | |
| | `pool_length` | int, default=8 | The length of the window for the average pooling operation. | |
| | `pool_stride` | int, default=8 | The stride of the average pooling operation. | |
| | `drop_prob_attn` | float, default=0.5 | The dropout rate for regularization for the attention layer. Values should be between 0 and 1. | |
| | `reduction_rate` | int, default=4 | The reduction rate used in the attention mechanism to reduce dimensionality and computational complexity. | |
| | `use_mlp` | bool, default=False | Flag to indicate whether an MLP (Multi-Layer Perceptron) should be used within the attention mechanism for further processing. | |
| | `freq_idx` | int, default=0 | DCT index used in fca attention mechanism. | |
| | `n_codewords` | int, default=4 | The number of codewords (clusters) used in attention mechanisms that employ quantization or clustering strategies. | |
| | `kernel_size` | int, default=9 | The kernel size used in certain types of attention mechanisms for convolution operations. | |
| | `activation` | type[nn.Module] = nn.ELU, | Activation function class to apply. Should be a PyTorch activation module class like `nn.ReLU` or `nn.ELU`. Default is `nn.ELU`. | |
| | `extra_params` | bool, default=False | Flag to indicate whether additional, custom parameters should be passed to the attention mechanism. | |
|
|
|
|
| ## References |
|
|
| 1. Wimpff, M., Gizzi, L., Zerfowski, J. and Yang, B., 2023. EEG motor imagery decoding: A framework for comparative analysis with channel attention mechanisms. arXiv preprint arXiv:2310.11198. |
| 2. Wimpff, M., Gizzi, L., Zerfowski, J. and Yang, B. GitHub https://github.com/martinwimpff/channel-attention (accessed 2024-03-28) |
|
|
|
|
| ## 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. |
|
|
