Explainable AI-Driven Neural Activity Analysis in Parkinsonian Rats under Electrical Stimulation

Abstract

Parkinson's disease (PD) is characterized by motor dysfunction and abnormal neural oscillations. This study combined graphene-based cortical stimulation with an explainable artificial intelligence (XAI) framework to investigate spatial and frequency-related features contributing to pre- and post-stimulation classification in Parkinsonian rats. Electrocorticogram (ECoG) signals were recorded before and after stimulation and classified using EEGNet and DeepConvNet. Layer-wise relevance propagation (LRP) and DeepLIFT were applied to identify frequency bands and recording channels contributing to the classification. The anatomical locations of these channels were mapped onto a cortical map. XAI analyses were performed separately for each animal to examine individual attribution patterns. Our XAI analysis revealed frequency-band- and region-specific contributions to pre- and post-stimulation classification in Parkinsonian rats. Our approach provided complementary insights into the features contributing to pre- and post-stimulation ECoG classification alongside PSD-based analyses. These findings highlight the potential of combining graphene-based cortical stimulation with explainable deep learning to identify classification-relevant features across frequency bands and cortical areas in pre- and post-stimulation recordings.