发表机构
Zhengzhou University; HumVerse (Zhengzhou) Technologies Co., Ltd.; Henan Provincial Key Laboratory of Brain Science and Brain-Computer Interface Technology; Xi’an Jiaotong University; University of Electronic Science and Technology of China; Chinese Academy of Medical Sciences（郑州大学; HumVerse（郑州）科技有限公司; 河南省脑科学与脑机接口技术重点实验室; 西安交通大学; 电子科技大学; 中国医学科学院）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
针对腕部运动神经肌肉异质且弱耦合导致传统脑电解码效果差的问题，提出ST-Topo GAN生成对抗网络，集成多频带表示、皮层拓扑建模与条件对抗学习，在腕部数据集上PCC达0.4436，优于基线。
AI 中文摘要
腕部通过实现精确的手部定位、力量调节和物体操作，在上肢功能中发挥着关键作用。连续脑-肌肉接口（BMIs）通过将神经活动解码为肌肉激活信号，为运动功能恢复提供了一种有前景的方法。然而，现有的脑电到肌电（EEG-to-EMG）模型主要针对肌肉协同相对稳定的任务开发，可能对腕部运动中涉及的异质且弱耦合的神经肌肉组织效果不佳。本文提出了ST-Topo GAN，一种用于腕部运动连续脑电到肌电解码的时空拓扑生成对抗网络。该框架集成了多频带脑电表示、感觉运动皮层拓扑建模和条件对抗学习，以重建多通道iEMG激活。该模型通过跨任务比较、腕部脑电到iEMG解码和消融实验进行了评估。与WAY-EEG-GAL抓取和提举数据集相比，腕部数据集表现出较低的肌肉间激活相似性和对传统模型更大的解码难度。ST-Topo GAN在腕部数据集上实现了平均PCC为0.4436，优于所有评估的基线模型，而消融研究证实了其关键组件的贡献。这些结果支持ST-Topo GAN在连续腕部脑电到iEMG解码中的有效性。
英文摘要
The wrist plays a critical role in upper-limb function by enabling precise hand positioning, force regulation, and object manipulation. Continuous brain--muscle interfaces (BMIs) offer a promising approach for motor restoration by decoding neural activity into muscle activation signals. However, existing EEG-to-EMG models have mainly been developed for tasks with relatively stable muscle synergies and may be less effective for the heterogeneous and weakly coupled neuromuscular organisation involved in wrist movements. This paper proposes ST-Topo GAN, a Spatial--Temporal Topological Generative Adversarial Network for continuous EEG-to-EMG decoding of wrist movements. The framework integrates multi-band EEG representation, sensorimotor cortical topology modelling, and conditional adversarial learning to reconstruct multi-channel iEMG activation. The model was evaluated through cross-task comparison, wrist EEG-to-iEMG decoding, and ablation experiments. Compared with the WAY-EEG-GAL grasp-and-lift dataset, the wrist dataset exhibited lower inter-muscle activation similarity and greater decoding difficulty for conventional models. ST-Topo GAN achieved an average PCC of 0.4436 on the wrist dataset, outperforming all evaluated baselines, while the ablation study confirmed the contribution of its key components. These results support the effectiveness of ST-Topo GAN for continuous wrist EEG-to-iEMG decoding.
Comments11 pages, 9 figures
