Neuroscience-Inspired Hierarchical GNN for Grasping Attempt Classification.

Brain-Computer Interfaces (BCI) have shown promise in facilitating upper limb rehabilitation following stroke. However, restoring fine hand functions, such as grasping, remains a significant challenge. To address this, we focus on decoding hand grasp attempts from electroencephalography (EEG) to enable BCI-driven hand rehabilitation. In this work, we propose several novel methods. First, inspired by the Small-World Brain Network Theory, we introduce a Small-world Hierarchical Interconnected Graph Neural Network (SHINE). SHINE captures transient power dynamics using multiscale convolution, overlapping windows, and learnable variance. It also simulates the characteristic architecture of the brain, where strong local connections coexist with weaker long-range links. This design advances existing Graph Neural Network (GNN) approaches, which typically model functional connectivity using a single, distance-agnostic metric, treating all brain regions uniformly. Second, we propose a Progressive Decay Graph (PDG) mechanism that progressively weakens long-range connections according to distance and training epoch, allowing the model's connectivity structure to evolve alongside the learning process. We evaluated SHINE on two EEG datasets comprising 50 healthy subjects and 19 post-stroke patients performing attempted hand opening and closing, which are the two complementary phases of a grasp. SHINE achieved superior performance over state-of-the-art methods, with improvements of 2.32% (healthy open vs. rest), 1.98% (healthy close vs. rest), 3.97% (stroke open vs. rest), and 2.66% (stroke close vs. rest) ($p< 0.01$ for all tasks), respectively.