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 :)