MSVTNet model from Liu K et al (2024) from [msvt2024]_.

ConvolutionRecurrentAttention/Transformer This model implements a multi-scale convolutional transformer network for EEG signal classification, as described in [msvt2024]_. .. figure:: https://raw.githubusercontent.com/SheepTAO/MSVTNet/refs/heads/main/MSVTNet_Arch.png :align: center :alt: MSVTNet Architecture Parameters ---------- n_filters_list : list[int], optional List of filter numbers for each TSConv block, by default (9, 9, 9, 9). conv1_kernels_size : list[int], optional List of kernel sizes for the first convolution in each TSConv block, by default (15, 31, 63, 125). conv2_kernel_size : int, optional Kernel size for the second convolution in TSConv blocks, by default 15. depth_multiplier : int, optional Depth multiplier for depthwise convolution, by default 2. pool1_size : int, optional Pooling size for the first pooling layer in TSConv blocks, by default 8. pool2_size : int, optional Pooling size for the second pooling layer in TSConv blocks, by default 7. drop_prob : float, optional Dropout probability for convolutional layers, by default 0.3. num_heads : int, optional Number of attention heads in the transformer encoder, by default 8. ffn_expansion_factor : float, optional Ratio to compute feedforward dimension in the transformer, by default 1. att_drop_prob : float, optional Dropout probability for the transformer, by default 0.5. num_layers : int, optional Number of transformer encoder layers, by default 2. activation : Type[nn.Module], optional Activation function class to use, by default nn.ELU. return_features : bool, optional Whether to return predictions from branch classifiers, by default False. Notes ----- This implementation is not guaranteed to be correct, has not been checked by original authors, only reimplemented based on the original code [msvt2024code]_. References ---------- .. [msvt2024] Liu, K., et al. (2024). MSVTNet: Multi-Scale Vision Transformer Neural Network for EEG-Based Motor Imagery Decoding. IEEE Journal of Biomedical an Health Informatics. .. [msvt2024code] Liu, K., et al. (2024). MSVTNet: Multi-Scale Vision Transformer Neural Network for EEG-Based Motor Imagery Decoding. Source Code: https://github.com/SheepTAO/MSVTNet .. 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 MSVTNet # Train your model model = MSVTNet(n_chans=22, n_outputs=4, n_times=1000) # ... training code ... # Push to the Hub model.push_to_hub( repo_id="username/my-msvtnet-model", commit_message="Initial model upload", ) **Loading a model from the Hub:** .. code:: from braindecode.models import MSVTNet # Load pretrained model model = MSVTNet.from_pretrained("username/my-msvtnet-model") # Load with a different number of outputs (head is rebuilt automatically) model = MSVTNet.from_pretrained("username/my-msvtnet-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 = MSVTNet.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.