Tian Miao, Li Shurui, Xu Ren, Cichocki Andrzej, Jin Jing
IEEE Trans Biomed Eng. 2025 Oct;72(10):2961-2971. doi: 10.1109/TBME.2025.3557255.
The motion trajectory prediction (MTP) based brain-computer interface (BCI) leverages electroencephalography (EEG) signals to reconstruct the three-dimensional trajectory of upper limb motion, which is pivotal for the advancement of prosthetic devices that can assist motor-disabled individuals. Most research focused on improving the performance of regression models while neglecting the correlation between the implicit information extracted from EEG features across various frequency bands with limb kinematics. Current work aims to identify key channels that capture information related to various motion execution movements from different frequency bands and reconstruct three-dimensional motion trajectories based on EEG features.
We propose an interpretable motion trajectory regression framework that extracts bandpower features from different frequency bands and concatenates them into multi-band fusion features. The extreme gradient boosting regression model with Bayesian optimization and Shapley additive explanation methods are introduced to provide further explanation.
The experimental results demonstrate that the proposed method achieves a mean Pearson correlation coefficient (PCC) value of 0.452, outperforming traditional regression models.
Our findings reveal that the contralateral side contributes the most to motion trajectory regression than the ipsilateral side which improves the clarity and interpretability of the motion trajectory regression model. Specifically, the feature from channel C5 in the Mu band is crucial for the movement of the right hand, while the feature from channel C3 in the Beta band plays a vital role.
This work provides a novel perspective on the comprehensive study of movement disorders.
基于运动轨迹预测（MTP）的脑机接口（BCI）利用脑电图（EEG）信号重建上肢运动的三维轨迹，这对于能够辅助运动功能障碍者的假肢装置的发展至关重要。大多数研究集中在提高回归模型的性能上，而忽略了从不同频段的EEG特征中提取的隐含信息与肢体运动学之间的相关性。当前的工作旨在识别从不同频段捕获与各种运动执行动作相关信息的关键通道，并基于EEG特征重建三维运动轨迹。
我们提出了一个可解释的运动轨迹回归框架，该框架从不同频段提取带功率特征，并将它们连接成多频段融合特征。引入了具有贝叶斯优化和Shapley附加解释方法的极端梯度提升回归模型，以提供进一步的解释。
实验结果表明，所提出的方法实现了0.452的平均皮尔逊相关系数（PCC）值，优于传统回归模型。
我们的研究结果表明，与同侧相比，对侧对运动轨迹回归的贡献最大，这提高了运动轨迹回归模型的清晰度和可解释性。具体而言，Mu频段中C5通道的特征对于右手运动至关重要，而Beta频段中C3通道的特征起着至关重要的作用。
这项工作为运动障碍的综合研究提供了一个新的视角。
