Pioneering EEG Motor Imagery Classification Through Counterfactual
  Analysis
Kang Yin Hye-Bin Shin Hee-Dong Kim Seong-Whan Lee
The application of counterfactual explanation (CE) techniques in the realm of
electroencephalography (EEG) classification has been relatively infrequent in
contemporary research. In this study, we attempt to introduce and explore a
novel non-generative approach to CE, specifically tailored for the analysis of
EEG signals. This innovative approach assesses the model's decision-making
process by strategically swapping patches derived from time-frequency analyses.
By meticulously examining the variations and nuances introduced in the
classification outcomes through this method, we aim to derive insights that can
enhance interpretability. The empirical results obtained from our experimental
investigations serve not only to validate the efficacy of our proposed approach
but also to reinforce human confidence in the model's predictive capabilities.
Consequently, these findings underscore the significance and potential value of
conducting further, more extensive research in this promising direction.