  Kinematic parameter (KP) estimation from early electroencephalogram (EEG)
signals is essential for positive augmentation using wearable robot. However,
work related to early estimation of KPs from surface EEG is sparse. In this
work, a deep learning-based model, BiCurNet, is presented for early estimation
of biceps curl using collected EEG signal. The model utilizes light-weight
architecture with depth-wise separable convolution layers and customized
attention module. The feasibility of early estimation of KPs is demonstrated
using brain source imaging. Computationally efficient EEG features in spherical
and head harmonics domain is utilized for the first time for KP prediction. The
best Pearson correlation coefficient (PCC) between estimated and actual
trajectory of $0.7$ is achieved when combined EEG features (spatial and
harmonics domain) in delta band is utilized. Robustness of the proposed network
is demonstrated for subject-dependent and subject-independent training, using
EEG signals with artifacts.
