Towards-Best-Practice-of-Interpreting-Deep-Learning-Models-for-EEG-based-BCI. In this project, we implemented 7 interpretation techniques on two benchmark deep learning models "EEGNet" and "InterpretableCNN" for EEG-based BCI. The methods include: gradient×input, DeepLIFT, integrated gradient, layer-wise relevance propagation (LRP), saliency map, deconvolution, and guided backpropagation

github.com/cuijiancorbin/Towards-Best-Practice-of-Interpreting-Deep-Learning-Models-for-EEG-based-BCI

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