  In non-invasive brain-computer interface systems, pre-movement decoding plays
an important role in the detection of movement before limbs actually move.
Movement-related cortical potential is a kind of brain activity associated with
pre-movement decoding. In current studies, patterns decoded from movement are
mainly applied to the binary classification between movement state and resting
state, such as elbow flexion and rest. The classifications between two movement
states and among multiple movement states are still challenging. This study
proposes a new method, the star-arrangement spectral filtering (SASF), to solve
the multi-class pre-movement classification problem. We first design a
referenced task-related component analysis (RTRCA) framework that consists of
two modules. This first module is the classification between movement state and
resting state; the second module is the classification of multiple movement
states. SASF is developed by optimizing the features in RTRCA. In SASF, feature
selection on filter banks is used on the first module of RTRCA, and feature
selection on time windows is used on the second module of RTRCA. A linear
discriminant analysis classifier is used to classify the optimized features. In
the binary classification between two motions, the classification accuracy of
SASF achieves 0.9670$\pm$0.0522, which is significantly higher than the result
provided by the deep convolutional neural network (0.6247$\pm$0.0680) and the
discriminative spatial pattern method (0.4400$\pm$0.0700). In the multi-class
classification of 7 states, the classification accuracy of SASF is
0.9491$\pm$0.0372. The proposed SASF greatly improves the classification
between two motions and enables the classification among multiple motions. The
result shows that the movement can be decoded from EEG signals before the
actual limb movement.
