Comparing Post-Hoc Explainable AI Methods for Interpreting Black-Box EEG Models in Depression Detection

arXiv:2605.28977v1 Announce Type: new Abstract: Recent advances in deep learning have enabled increasingly accurate electroencephalography (EEG)-based classification of Major Depressive Disorder (MDD), but the decision-making processes of high-capacity models remain difficult to interpret. This study investigates multiple post-hoc explainability methods applied to an InceptionTime architecture trained for EEG-based MDD detection. The analysis includes Shapley-based, gradient-based, and perturbation-based attribution approaches: DeepSHAP, Integrated Gradients, GradCAM, Occlusion, and Permutatio

Source: arXiv cs.LG — read the full report at the original publisher.

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