  Deep learning is widely used to decode the electroencephalogram (EEG) signal.
However, there are few attempts to specifically investigate how to explain the
EEG-based deep learning models. We conduct a review to summarize the existing
works explaining the EEG-based deep learning model. Unfortunately, we find that
there is no appropriate method to explain them. Based on the characteristic of
EEG data, we suggest a context-aware perturbation method to generate a saliency
map from the perspective of the raw EEG signal. Moreover, we also justify that
the context information can be used to suppress the artifacts in the EEG-based
deep learning model. In practice, some users might want a simple version of the
explanation, which only indicates a few features as salient points. To this
end, we propose an optional area limitation strategy to restrict the
highlighted region. To validate our idea and make a comparison with the other
methods, we select three representative EEG-based models to implement
experiments on the emotional EEG dataset DEAP. The results of the experiments
support the advantages of our method.
