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 performance, we introduce a Hybrid Temporal-Spectral Reward (HTSR) loss function, which integrates time-domain, frequency-domain, and reward-based loss components. In addition, we contribute a new Gait-EEG Dataset (GED), consisting of synchronized EEG and lower-limb joint angle data collected from 50 participants across two laboratory visits. Extensive experiments demonstrate that EEG2GAIT with HTSR achieves superior performance on the GED dataset, reaching a Pearson correlation coefficient (r) of 0.959, a coefficient of determination of 0.914, and a Mean Absolute Error (MAE) of 0.193. On the MoBI dataset, EEG2GAIT likewise consistently outperforms existing methods, achieving an r of 0.779, a coefficient of determination of 0.597, and an MAE of 4.384. Statistical analyses confirm that these improvements are significant compared to all prior models. Ablation studies further validate the contributions of the hierarchical GCN modules and the proposed HTSR loss, while saliency analysis highlights the involvement of motor-related brain regions in decoding tasks. Collectively, these findings underscore EEG2GAIT's potential for advancing brain-computer interface applications, particularly in lower-limb rehabilitation and assistive technologies.

翻译：从脑电图（EEG）信号解码步态动力学面临重大挑战，这源于运动过程的复杂空间依赖性、对精确时域和频域特征提取的需求，以及高质量步态脑电数据集的稀缺。为解决这些问题，我们提出了EEG2GAIT，这是一种新颖的基于分层图的模型，它使用分层图卷积网络（GCN）金字塔来捕获脑电通道的多层次空间嵌入。为了进一步提升解码性能，我们引入了混合时域-频域奖励（HTSR）损失函数，该函数整合了时域、频域和基于奖励的损失分量。此外，我们贡献了一个新的步态-脑电数据集（GED），该数据集包含从50名参与者在两次实验室访问中收集的同步脑电图和下肢关节角度数据。大量实验表明，采用HTSR的EEG2GAIT在GED数据集上取得了卓越的性能，达到了皮尔逊相关系数（r）0.959、决定系数0.914和平均绝对误差（MAE）0.193。在MoBI数据集上，EEG2GAIT同样持续优于现有方法，实现了r值0.779、决定系数0.597和MAE 4.384。统计分析证实，与所有先前模型相比，这些改进具有显著性。消融研究进一步验证了分层GCN模块和所提出的HTSR损失的贡献，而显著性分析则突显了运动相关脑区在解码任务中的参与。总的来说，这些发现强调了EEG2GAIT在推进脑机接口应用方面的潜力，特别是在下肢康复和辅助技术领域。