EEG2GAIT: A Hierarchical Graph Convolutional Network for EEG-based Gait Decoding
Journal:
arXiv
Published Date:
Apr 2, 2025
Abstract
Decoding gait dynamics from EEG signals presents significant challenges due
to the complex spatial dependencies of motor processes, the need for accurate
temporal and spectral feature extraction, and the scarcity of high-quality gait
EEG datasets. To address these issues, we propose EEG2GAIT, a novel
hierarchical graph-based model that captures multi-level spatial embeddings of
EEG channels using a Hierarchical Graph Convolutional Network (GCN) Pyramid. To
further improve decoding accuracy, we introduce a Hybrid Temporal-Spectral
Reward (HTSR) loss function, which combines time-domain, frequency-domain, and
reward-based loss components. Moreover, we contribute a new Gait-EEG Dataset
(GED), consisting of synchronized EEG and lower-limb joint angle data collected
from 50 participants over two lab visits. Validation experiments on both the
GED and the publicly available Mobile Brain-body imaging (MoBI) dataset
demonstrate that EEG2GAIT outperforms state-of-the-art methods and achieves the
best joint angle prediction. Ablation studies validate the contributions of the
hierarchical GCN modules and HTSR Loss, while saliency maps reveal the
significance of motor-related brain regions in decoding tasks. These findings
underscore EEG2GAIT's potential for advancing brain-computer interface
applications, particularly in lower-limb rehabilitation and assistive
technologies.
