Novel Approach Explains Spatio-Spectral Interactions In Raw Electroencephalogram Deep Learning Classifiers

The application of deep learning classifiers to resting-state electroencephalography (rs-EEG) data has become increasingly common. However, relative to studies using traditional machine learning methods and extracted features, deep learning methods are less explainable. A growing number of studies have presented explainability approaches for rs-EEG deep learning classifiers. However, to our knowledge, no approaches give insight into spatio-spectral interactions (i.e., how spectral activity in one channel may interact with activity in other channels). In this study, we combine gradient and perturbation-based explainability approaches to give insight into spatio-spectral interactions in rs-EEG deep learning classifiers for the first time. We present the approach within the context of major depressive disorder (MDD) diagnosis identifying effects upon frontal $\delta$ activity, upon frontal interactions, and upon widespread $\gamma_{{\mathrm {high}}}$ interactions. Our approach provides novel insights and represents a significant step forward for the field of explainable EEG classification.