  Kinematics decoding from brain activity helps in developing rehabilitation or
power-augmenting brain-computer interface devices. Low-frequency signals
recorded from non-invasive electroencephalography (EEG) are associated with the
neural motor correlation utilised for motor trajectory decoding (MTD). In this
communication, the ability to decode motor kinematics trajectory from
pre-movement delta-band (0.5-3 Hz) EEG is investigated for the healthy
participants. In particular, two deep learning-based neural decoders called
PreMovNet-I and PreMovNet-II, are proposed that make use of motor-related
neural information existing in the pre-movement EEG data. EEG data segments
with various time lags of 150 ms, 200 ms, 250 ms, 300 ms, and 350 ms before the
movement onset are utilised for the same. The MTD is presented for
grasp-and-lift task (WAY-EEG-GAL dataset) using EEG with the various lags taken
as input to the neural decoders. The performance of the proposed decoders are
compared with the state-of-the-art multi-variable linear regression (mLR)
model. Pearson correlation coefficient and hand trajectory are utilised as
performance metric. The results demonstrate the viability of decoding 3D hand
kinematics using pre-movement EEG data, enabling better control of BCI-based
external devices such as exoskeleton/exosuit.
