Graph Neural Networks Uncover Geometric Neural Representations in Reinforcement-Based Motor Learning

TL;DR

This study uses Graph Neural Networks to analyze EEG data, revealing stable, geometry-based neural signatures related to reinforcement motor learning, which enhance understanding of brain activity during complex, real-world tasks.

Contribution

It introduces a GNN-based approach to uncover geometric neural representations in EEG data during reinforcement motor learning, demonstrating robustness and invariance of neural signatures across conditions.

Findings

GNNs effectively capture spatial neural patterns during motor learning

Neural signatures are stable and persist across different pretraining strategies

Geometric neural structures show partial invariance to task transformations

Abstract

Graph Neural Networks (GNN) can capture the geometric properties of neural representations in EEG data. Here we utilise those to study how reinforcement-based motor learning affects neural activity patterns during motor planning, leveraging the inherent graph structure of EEG channels to capture the spatial relationships in brain activity. By exploiting task-specific symmetries, we define different pretraining strategies that not only improve model performance across all participant groups but also validate the robustness of the geometric representations. Explainability analysis based on the graph structures reveals consistent group-specific neural signatures that persist across pretraining conditions, suggesting stable geometric structures in the neural representations associated with motor learning and feedback processing. These geometric patterns exhibit partial invariance to certain task space transformations, indicating symmetries that enable generalisation across conditions while maintaining specificity to individual learning strategies. This work demonstrates how GNNs can uncover the effects of previous outcomes on motor planning, in a complex real-world task, providing insights into the geometric principles governing neural representations. Our experimental design bridges the gap between controlled experiments and ecologically valid scenarios, offering new insights into the organisation of neural representations during naturalistic motor learning, which may open avenues for exploring fundamental principles governing brain activity in complex tasks.