Aggregating XAI-based explanations to identify spectral-spatial patterns in CNN-based resting-state EEG...

Convolutional neural networks (CNNs) achieve high performance in electroencephalographic (EEG) classification tasks; however, their decision-making mechanisms remain difficult to interpret. Explainable artificial intelligence (XAI) methods are typically applied to provide insight into individual model decisions, yet such explanations do not reveal the overall structure of the patterns learned by the network. In this study, we hypothesize that, in EEG analysis, XAI can serve a deeper role: when appropriately applied, it can expose the general patterns learned by a trained CNN, thereby transforming it from a purely predictive model into a framework capable of revealing candidate discriminative structures that may form the basis for future neuroscientific hypotheses. This shift is enabled by structured aggregation of local explanations, through which instance-level insights are consolidated into cross-subject patterns. To implement this strategy, we employ averagedLIME, an extension of Local Interpretable Model-agnostic Explanations, which aggregates sample-level explanations into global class-level saliency maps. In trial-based EEG paradigms, such aggregation reinforces consistent patterns across samples analogously to event-related potentials averaging. In this study, we examine whether this strategy remains effective in a temporally unaligned resting-state setting, taking subject-independent classification of Alzheimer's disease and cognitively normal controls as a case study. Four neural architectures are compared, with spectral CNN yielding the most robust performance (95.81% test accuracy). The best-performing model is subsequently analyzed using averagedLIME, while SHapley Additive exPlanations (SHAP) and Grad-CAM are employed as complementary explanation techniques. Quantitative analyses demonstrated that the averagedLIME patterns were stable across cross-validation folds, robust to perturbation parameter selection, reproducible on unseen test data, and strongly supported by independent SHAP explanations. When interpreted in conjunction with the averaged input representations, the resulting saliency maps reproduced established EEG slowing patterns in Alzheimer's disease, while also highlighting localized spatial-spectral structures that were less apparent in the averaged EEG representations alone. These findings demonstrate that structured aggregation of CNN explanations enables extraction of stable cross-subject patterns from resting-state EEG and may reveal candidate discriminative structures that can motivate future neuroscientific hypotheses.