发表机构
IIT Gandhinagar（印度理工学院甘地纳加尔分校）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
提出一种数据驱动的EEG-EMG通道对选择框架，将选择问题建模为双目标优化，用NSGA-II求解，在八名中风患者数据上达到89.6%平均分类准确率。
AI 中文摘要
整合脑电图（EEG）和肌电图（EMG）信号的混合脑机接口（BCI）系统在提高运动想象（MI）分类可靠性方面显示出巨大潜力，尤其是在神经康复应用中。然而，识别能够有效捕获皮质肌相互作用的 informative EEG-EMG 通道对仍然是一个具有挑战性的问题，因为现有方法通常依赖于手动预定义的通道组合，这些组合可能无法在不同受试者之间泛化。在本工作中，提出了一种数据驱动的 EEG-EMG 对选择框架，其中通道对选择被表述为一个受约束的双目标优化问题。所提出的方法联合最大化 EEG 通道相对于运动皮层区域的空间相关性以及 EEG 与 EMG 信号之间的皮质肌耦合强度，并使用 NSGA-II 求解，以自动识别最优的通道对子集。为了提取判别性特征，将捕获 EEG-EMG 相互作用的带功率时间特征之间的相关性与基于 ERD 的 EEG 特征相结合，并采用基于滑动窗口的时间分析来考虑 MI 信号的动态特性。所提出的框架在八名中风患者的 MI 数据上进行了评估，实现了 89.6% 的平均分类准确率，证明了其在捕获生理上有意义的皮质肌相互作用和提高分类性能方面的有效性。
英文摘要
Hybrid brain-computer interface (BCI) systems that integrate electroencephalography (EEG) and electromyography (EMG) signals have shown significant potential in improving the reliability of motor imagery (MI) classification, particularly in neuro-rehabilitation applications. However, identifying informative EEG-EMG channel pairs that effectively capture corticomuscular interactions remains a challenging problem, as existing approaches typically rely on manually predefined channel combinations that may not generalise across subjects. In this work, a data-driven EEG-EMG pair selection framework is proposed, in which channel pair selection is formulated as a constrained bi-objective optimisation problem. The proposed method jointly maximises the spatial relevance of EEG channels with respect to motor cortex regions and the corticomuscular coupling strength between EEG and EMG signals, and is solved using the NSGA-II to automatically identify an optimal subset of pairs. To extract discriminative features, the correlation between band-power time features capturing EEG-EMG interaction is combined with ERD-based EEG features, and a sliding-window-based temporal analysis is employed to account for the dynamic nature of MI signals. The proposed framework is evaluated on MI data from eight stroke patients and achieves an average classification accuracy of 89.6%, demonstrating its effectiveness in capturing physiologically meaningful corticomuscular interactions and improving classification performance.
Comments6 pages, 5 figures, 1 table, accepted at Brain-Machine Interface (BMI) Systems Session, IEEE International Conference on Systems, Man, and Cybernetics (IEEE SMC 2026)
