  Brain-computer interface (BCI) systems can be utilized for kinematics
decoding from scalp brain activation to control rehabilitation or
power-augmenting devices. In this study, the hand kinematics decoding for grasp
and lift task is performed in three-dimensional (3D) space using scalp
electroencephalogram (EEG) signals. Twelve subjects from the publicly available
database WAY-EEG-GAL, has been utilized in this study. In particular,
multi-layer perceptron (MLP) and convolutional neural network-long short-term
memory (CNN-LSTM) based deep learning frameworks are proposed that utilize the
motor-neural information encoded in the pre-movement EEG data. Spectral
features are analyzed for hand kinematics decoding using EEG data filtered in
seven frequency ranges. The best performing frequency band spectral features
has been considered for further analysis with different EEG window sizes and
lag windows. Appropriate lag windows from movement onset, make the approach
pre-movement in true sense. Additionally, inter-subject hand trajectory
decoding analysis is performed using leave-one-subject-out (LOSO) approach. The
Pearson correlation coefficient and hand trajectory are considered as
performance metric to evaluate decoding performance for the neural decoders.
This study explores the feasibility of inter-subject 3-D hand trajectory
decoding using EEG signals only during reach and grasp task, probably for the
first time. The results may provide the viable information to decode 3D hand
kinematics using pre-movement EEG signals for practical BCI applications such
as exoskeleton/exosuit and prosthesis.
