About Open source code of paper:
-Toward reliable signals decoding for electroencephalogram: A benchmark study to EEGNeX.
-https://arxiv.org/abs/2207.12369

This notebook is released for easy implementation of running all benchmarkmodels designed for electroencephalography(EEG) classification tasks, including:

• Single_LSTM
• Single_GRU
• OneD_CNN
• OneD_CNN_Dilated
• OneD_CNN_Causal
• OneD_CNN_CausalDilated
• TwoD_CNN
• TwoD_CNN_Dilated
• TwoD_CNN_Separable
• TwoD_CNN_Depthwise
• CNN_LSTM
• CNN_GRU
• Single_ConvLSTM2D
• EEGNet_4_2
• EEGNet_8_2
• EEGNeX_8_32

For running the code, please run notebook Run_model.ipynb

Additional python packages required:

• keras 2.8.0
• tensorflow 2.8.0
• torch 1.10.2

The result folder contains validation results of running benchmarkmodels on four EEG datasets from paper.

EEGNeX architecture

More models are planned to be added:

• DeepConvNet
• ShallowConvNet
• SNN(Spike neural network)_based models

We also welcome you to contribute any model resources/papers in the discussion for our future plan :)