  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.
