selfeeg.models
This module collects various Deep Learning models and custom layers. It is divided in two submodules:
layers: a collection custom layers with the possibility to add norm constraints.
zoo: a collection of deep learning models proposed for EEG applications.
models.layers module
Classes
nn.Conv1d layer with norm constraints. |
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nn.conv2d layer with norm constraints. |
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nn.Linear layer with norm constraints. |
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Depthwise 2D layer with norm constraints. |
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Filter bank layer of the FBCNet model. |
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Separable Convolutional layer with norm constraints. |
models.encoders module
Classes
Pytorch Implementation of the DeepConvNet Encoder. |
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Pytorch implementation of the EEGConformer Encoder. |
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Pytorch Implementation of the EEGInception Encoder. |
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Pytorch Implementation of the EEGnet Encoder. |
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Pytorch implementation of the EEGSym Encoder. |
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Pytorch implementation of the FBCNet Encoder. |
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Pytorch implementation of the Resnet Encoder |
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Pytorch implementation of the ShallowNet Encoder. |
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Pytorch implementation of the StagerNet Encoder. |
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Pytorch implementation of the STNet Encoder. |
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Pytorch Implementation of the TinySleepNet Encoder. |
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Pytorch implementation of the xEEGNet Encoder. |
models.zoo module
Classes
Pytorch implementation of the ATCNet model. |
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Pytorch Implementation of the DeepConvNet model. |
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Pytorch implementation of EEGConformer. |
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Pytorch Implementation of the EEGInception model. |
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Pytorch implementation of the EEGNet model. |
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Pytorch implementation of the EEGSym model. |
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Pytorch implementation of the FBCNet model. |
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Pytorch implementation of the Resnet model |
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Pytorch implementation of the ShallowNet model. |
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Pytorch implementation of the StagerNet model. |
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Pytorch implementation of the STNet model. |
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Pytorch Implementation of the TinySleepNet model. |
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Pytorch implementation of xEEGNet. |