Jain Anant, Kumar Lalan
Department of Electrical Engineering, Indian Institute of Technology Delhi, New Delhi 110016, India.
Department of Electrical Engineering, Indian Institute of Technology Delhi, New Delhi 110016, India; Bharti School of Telecommunication, Indian Institute of Technology Delhi, New Delhi 110016, India; Yardi School of Artificial Intelligence, Indian Institute of Technology Delhi, New Delhi 110016, India.
Comput Biol Med. 2025 Mar;186:109608. doi: 10.1016/j.compbiomed.2024.109608. Epub 2024 Dec 29.
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 the time lag and window size of 100ms and 450ms, 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 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.
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（如外骨骼套装、假肢和康复设备）的一个活跃研究领域。然而，基于EEG源成像（ESI）的运动学预测在文献中鲜有探讨。
在本研究中，运动前EEG特征被用于预测抓握和提起运动任务的三维（3D）手部运动学。一个公共数据集WAY-EEG-GAL被用于MKP分析。特别是，探索了基于额顶叶区域的传感器域（EEG数据）和源域（ESI数据）特征用于MKP。探索了基于深度学习的模型以实现高效的运动学解码。分析了各种时间滞后和窗口大小用于手部运动学预测。随后，进行了受试者内和受试者间的MKP分析，以研究神经解码器的受试者特异性和非受试者特异性运动学习能力。皮尔逊相关系数（PCC）被用作运动学轨迹解码的性能指标。
rEEGNet神经解码器在分别具有100ms和450ms时间滞后及窗口大小的传感器域和源域特征下取得了最佳性能。使用传感器域特征在x、y和z方向上分别实现了0.790、0.795和0.637的最高平均PCC值，而使用源域特征分别实现了0.769、0.777和0.647。
本研究探索了使用EEG传感器域和源域特征进行抓握和提起任务轨迹预测的可行性。此外，使用提出的具有EEG源域特征的深度学习解码器进行了受试者间轨迹估计。
