脑电情绪识别所用到深度学习模型 EEG models(BiGRU,lstm,cnn,gcn,dnn,rnn等等).zip

<p align="center"> <a href="https://github.com/SuperBruceJia/EEG-DL"> <img width="500px" src="https://github.com/SuperBruceJia/EEG-DL/raw/master/Logo.png"></a> <br /> <br /> <a href="https://gitter.im/EEG-DL/community"><img alt="Chat on Gitter" src="https://img.shields.io/gitter/room/nwjs/nw.js.svg" /></a> <a href="https://www.anaconda.com/"><img alt="Python Version" src="https://img.shields.io/badge/Python-3.x-green.svg" /></a> <a href="https://www.tensorflow.org/install"><img alt="TensorFlow Version" src="https://img.shields.io/badge/TensorFlow-1.13.1-red.svg" /></a> <a href="https://github.com/SuperBruceJia/EEG-DL/blob/master/LICENSE"><img alt="MIT License" src="https://img.shields.io/badge/license-MIT-blue.svg" /></a> </p> <!-- <div align="center"> <a href="https://github.com/SuperBruceJia/EEG-DL"> <img width="500px" src="https://github.com/SuperBruceJia/EEG-DL/raw/master/Logo.png"></a> </div> --> -------------------------------------------------------------------------------- # Welcome to EEG Deep Learning Library **EEG-DL** is a Deep Learning (DL) library written by [TensorFlow](https://www.tensorflow.org) for EEG Tasks (Signals) Classification. It provides the latest DL algorithms and keeps updated. <!-- [](https://gitter.im/EEG-DL/community) [](https://www.anaconda.com/) [](https://www.tensorflow.org/install) [](https://github.com/SuperBruceJia/EEG-DL/blob/master/LICENSE) --> ## Table of Contents <ul> <li><a href="#Documentation">Documentation</a></li> <li><a href="#Usage-Demo">Usage Demo</a></li> <li><a href="#Notice">Notice</a></li> <li><a href="#Research-Ideas">Research Ideas</a></li> <li><a href="#Common-Issues">Common Issues</a></li> <li><a href="#Structure-of-the-code">Structure of the code</a></li> <li><a href="#Citation">Citation</a></li> <li><a href="#Other-Useful-Resources">Other Useful Resources</a></li> <li><a href="#Contribution">Contribution</a></li> <li><a href="#Organizations">Organizations</a></li> </ul> ## Documentation **The supported models** include | No. | Model | Codes | | :----:| :----: | :----: | | 1 | Deep Neural Networks | [DNN](https://github.com/SuperBruceJia/EEG-DL/blob/master/Models/Network/DNN.py) | | 2 | Convolutional Neural Networks [[Paper]](https://iopscience.iop.org/article/10.1088/1741-2552/ab4af6/meta) [[Tutorial]](https://github.com/SuperBruceJia/EEG-Motor-Imagery-Classification-CNNs-TensorFlow)| [CNN](https://github.com/SuperBruceJia/EEG-DL/blob/master/Models/Network/CNN.py) | | 3 | Deep Residual Convolutional Neural Networks [[Paper]](https://www.cv-foundation.org/openaccess/content_cvpr_2016/papers/He_Deep_Residual_Learning_CVPR_2016_paper.pdf) | [ResNet](https://github.com/SuperBruceJia/EEG-DL/blob/master/Models/Network/ResCNN.py) | | 4 | Thin Residual Convolutional Neural Networks [[Paper]](https://arxiv.org/abs/1902.10107) | [Thin ResNet](https://github.com/SuperBruceJia/EEG-DL/blob/master/Models/Network/Thin_ResNet.py) | | 5 | Densely Connected Convolutional Neural Networks [[Paper]](https://arxiv.org/abs/1608.06993) | [DenseNet](https://github.com/SuperBruceJia/EEG-DL/blob/master/Models/Network/DenseCNN.py) | | 6 | Fully Convolutional Neural Networks [[Paper]](https://www.cv-foundation.org/openaccess/content_cvpr_2015/papers/Long_Fully_Convolutional_Networks_2015_CVPR_paper.pdf) | [FCN](https://github.com/SuperBruceJia/EEG-DL/blob/master/Models/Network/Fully_Conv_CNN.py) | | 7 | One Shot Learning with Siamese Networks (CNNs Backbone) <br> [[Paper]](https://www.cs.cmu.edu/~rsalakhu/papers/oneshot1.pdf) [[Tutorial]](https://towardsdatascience.com/one-shot-learning-with-siamese-networks-using-keras-17f34e75bb3d) | [Siamese Networks](https://github.com/SuperBruceJia/EEG-DL/blob/master/Models/Network/Siamese_Network.py) | | 8 | Graph Convolutional Neural Networks <br> [[Paper]](https://arxiv.org/abs/2006.08924) [[Presentation]](https://drive.google.com/file/d/1ecMbtZV2eH14sRAqWIIf1iRvDAC7DMDs/view?usp=sharing) [[Tutorial]](https://github.com/mdeff/cnn_graph) <br> [[GCN / GNN Summary for Chinese Readers]](https://github.com/wangyouze/GNN-algorithms) <br> [[GNN-related Algorithms Review for Chinese Readers]](https://github.com/LYuhang/GNN_Review) <br> [[Literature of Deep Learning for Graphs]](https://github.com/DeepGraphLearning/LiteratureDL4Graph) | [GCN / Graph CNN](https://github.com/SuperBruceJia/EEG-DL/blob/master/Models/Network/lib_for_GCN/GCN_Model.py) | | 9 | Graph Convolutional Neural Networks <br> (Pure Python Implementation from [Reza Amini](https://github.com/magnumical)) | [GCN / Graph CNN](https://github.com/magnumical/GCN_for_EEG) | | 10 | Deep Residual Graph Convolutional Neural Networks [[Paper]](https://arxiv.org/abs/2007.13484) | [ResGCN](https://github.com/SuperBruceJia/EEG-DL/blob/master/Models/Network/lib_for_GCN/ResGCN_Model.py) | | 11 | Densely Connected Graph Convolutional Neural Networks | [DenseGCN](https://github.com/SuperBruceJia/EEG-DL/blob/master/Models/Network/lib_for_GCN/DenseGCN_Model.py) | | 12 | Bayesian Convolutional Neural Network <br> via Variational Inference <br> [[Paper]](https://arxiv.org/abs/1901.02731) [[Thesis]](https://github.com/kumar-shridhar/Master-Thesis-BayesianCNN/raw/master/thesis.pdf) <br> (PyTorch Implementation by [Kumar Shridhar](https://github.com/kumar-shridhar)) <br> [[Latest Codes]](https://github.com/kumar-shridhar/PyTorch-BayesianCNN) | [Bayesian CNNs](https://github.com/SuperBruceJia/EEG-BayesianCNN) | | 13 | Recurrent Neural Networks [[Paper]](https://arxiv.org/abs/2005.00777) | [RNN](https://github.com/SuperBruceJia/EEG-DL/blob/master/Models/Network/RNN.py) | | 14 | Attention-based Recurrent Neural Networks [[Paper]](https://arxiv.org/abs/2005.00777) | [RNN with Attention](https://github.com/SuperBruceJia/EEG-DL/blob/master/Models/Network/RNN_with_Attention.py) | | 15 | Bidirectional Recurrent Neural Networks [[Paper]](https://arxiv.org/abs/2005.00777) | [BiRNN](https://github.com/SuperBruceJia/EEG-DL/blob/master/Models/Network/BiRNN.py) | | 16 | Attention-based Bidirectional Recurrent Neural Networks [[Paper]](https://arxiv.org/abs/2005.00777) | [BiRNN with Attention](https://github.com/SuperBruceJia/EEG-DL/blob/master/Models/Network/BiRNN_with_Attention.py) | | 17 | Long-short Term Memory [[Paper]](https://arxiv.org/abs/2005.00777) | [LSTM](https://github.com/SuperBruceJia/EEG-DL/blob/master/Models/Network/LSTM.py) | | 18 | Attention-based Long-short Term Memory [[Paper]](https://arxiv.org/abs/2005.00777) | [LSTM with Attention](https://github.com/SuperBruceJia/EEG-DL/blob/master/Models/Network/LSTM_with_Attention.py) | | 19 | Bidirectional Long-short Term Memory [[Paper]](https://arxiv.org/abs/2005.00777) | [BiLSTM](https://github.com/SuperBruceJia/EEG-DL/blob/master/Models/Network/BiLSTM.py) | | 20 | Attention-based Bidirectional Long-short Term Memory [[Paper]](https://arxiv.org/abs/2005.00777) | [BiLSTM with Attention](https://github.com/SuperBruceJia/EEG-DL/blob/master/Models/Network/BiLSTM_with_Attention.py) | | 21 | Gated Recurrent Unit [[Paper]](https://arxiv.org/abs/2005.00777) | [GRU](https://github.com/SuperBruceJia/EEG-DL/blob/master/Models/Network/GRU.py) | | 22 | Attention-based Gated Recurrent Unit [[Paper]](https://arxiv.org/abs/2005.00777) | [GRU with Attention](https://github.com/SuperBruceJia/EEG-DL/blob/master/Models/Network/GRU_with_Attention.py) | | 23 | Bidirectional Gated Recurrent Unit [[Paper]](https://arxiv.org/abs/2005.00777) | [BiGRU](https://github.com/SuperBruceJia/EEG-DL/blob/master/Models/N