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

TL;DR

This study employs explainable AI and deep learning to analyze neural activity in Parkinsonian rats, revealing spatial and frequency-specific insights that improve understanding and monitoring of PD beyond traditional statistical methods.

Contribution

It introduces an XAI approach combined with CNNs and graphene electrodes for unbiased neural activity analysis in PD, enabling real-time monitoring and targeted insights.

Findings

XAI identified key neural inputs in beta and gamma bands.

Graphene electrodes enabled less-invasive, high-quality recordings.

Deep learning classification distinguished pre- and post-stimulation states.

Abstract

Parkinson's disease (PD) is a neurodegenerative disorder characterized by motor dysfunction and abnormal neural oscillations. These symptoms can be modulated through electrical stimulation. Traditional neural activity analysis in PD has typically relied on statistical methods, which often introduce bias owing to the need for expert-driven feature extraction. To address this limitation, we explore an explainable artificial intelligence (XAI) approach to analyze neural activity in Parkinsonian rats receiving electrical stimulation. Electrocorticogram (ECoG) signals were collected before and after electrical stimulation using graphene-based electrodes that enable less-invasive monitoring and stimulation in PD. EEGNet, a convolutional neural network, classified these ECoG signals into pre- and post-stimulation states. We applied layer-wise relevance propagation, an XAI technique, to identify key neural inputs contributing to the model's decisions, incorporating the spatial electrode information matched to the cortex map. The XAI analysis highlighted area-specific importance in beta and gamma frequency bands, which could not be detected through mean comparison analyses relying on feature extraction. These findings demonstrate the potential of XAI in analyzing neural dynamics in neurodegenerative disorders such as PD, suggesting that the integration of graphene-based electrodes with advanced deep learning models offers a promising solution for real-time PD monitoring and therapy.