Electroencephalogram (EEG) signals-based motor kinematics prediction (MKP) has been an active area of research to develop brain-computer interface (BCI) systems such as exosuits, prostheses, and rehabilitation devices. However, EEG source imaging (ESI) based kinematics prediction is sparsely explored in the literature. In this study, pre-movement EEG features are utilized to predict three-dimensional (3D) hand kinematics for the grasp-and-lift motor task. A public dataset, WAY-EEG-GAL, is utilized for MKP analysis. In particular, sensor-domain (EEG data) and source-domain (ESI data) based features from the frontoparietal region are explored for MKP. Deep learning-based models are explored to achieve efficient kinematics decoding. Various time-lagged and window sizes are analyzed for hand kinematics prediction. Subsequently, intra-subject and inter-subject MKP analysis is performed to investigate the subject-specific and subject-independent motor-learning capabilities of the neural decoders. The Pearson correlation coefficient (PCC) is used as the performance metric for kinematics trajectory decoding. The rEEGNet neural decoder achieved the best performance with sensor-domain and source-domain features with a time lag and window size of 100 ms and 450 ms, respectively. The highest mean PCC values of 0.790, 0.795, and 0.637 are achieved using sensor-domain features, while 0.769, 0.777, and 0.647 are achieved using source-domain features in x, y, and z-directions, respectively. This study explores the feasibility of trajectory prediction using EEG sensor-domain and source-domain EEG features for the grasp-and-lift task. Furthermore, inter-subject trajectory estimation is performed using the proposed deep learning decoder with EEG source domain features.

翻译：基于脑电图（EEG）信号的运动学预测（MKP）一直是开发脑机接口（BCI）系统（如外骨骼、假肢和康复设备）的活跃研究领域。然而，基于脑电源成像（ESI）的运动学预测在文献中鲜有探索。本研究利用运动前EEG特征来预测抓握与提举运动任务中的三维（3D）手部运动学。研究采用公共数据集WAY-EEG-GAL进行MKP分析。具体而言，我们探索了来自额顶叶区域的传感器域（EEG数据）和源域（ESI数据）特征用于MKP。研究探索了基于深度学习的模型以实现高效的运动学解码。分析了不同时间滞后和窗口大小对手部运动学预测的影响。随后，进行了受试者内和受试者间MKP分析，以探究神经解码器的受试者特异性和受试者无关的运动学习能力。皮尔逊相关系数（PCC）被用作运动学轨迹解码的性能指标。rEEGNet神经解码器在使用传感器域和源域特征（时间滞后和窗口大小分别为100毫秒和450毫秒）时取得了最佳性能。使用传感器域特征在x、y和z方向上分别获得了0.790、0.795和0.637的最高平均PCC值，而使用源域特征则分别获得了0.769、0.777和0.647的值。本研究探讨了利用EEG传感器域和源域特征进行抓握与提举任务轨迹预测的可行性。此外，利用所提出的深度学习解码器结合EEG源域特征进行了受试者间轨迹估计。