  The recent rise of EEG-based end-to-end deep learning models presents a
significant challenge in elucidating how these models process raw EEG signals
and generate predictions in the frequency domain. This challenge limits the
transparency and credibility of EEG-based end-to-end models, hindering their
application in security-sensitive areas. To address this issue, we propose a
mask perturbation method to explain the behavior of end-to-end models in the
frequency domain. Considering the characteristics of EEG data, we introduce a
target alignment loss to mitigate the out-of-distribution problem associated
with perturbation operations. Additionally, we develop a perturbation generator
to define perturbation generation in the frequency domain. Our explanation
method is validated through experiments on multiple representative end-to-end
deep learning models in the EEG decoding field, using an established EEG
benchmark dataset. The results demonstrate the effectiveness and superiority of
our method, and highlight its potential to advance research in EEG-based
end-to-end models.
