  Brain-computer interfaces (BCIs), transform neural signals in the brain into
in-structions to control external devices. However, obtaining sufficient
training data is difficult as well as limited. With the advent of advanced
machine learning methods, the capability of brain-computer interfaces has been
enhanced like never before, however, these methods require a large amount of
data for training and thus require data augmentation of the limited data
available. Here, we use spiking neural networks (SNN) as data generators. It is
touted as the next-generation neu-ral network and is considered as one of the
algorithms oriented to general artifi-cial intelligence because it borrows the
neural information processing from bio-logical neurons. We use the SNN to
generate neural spike information that is bio-interpretable and conforms to the
intrinsic patterns in the original neural data. Ex-periments show that the
model can directly synthesize new spike trains, which in turn improves the
generalization ability of the BCI decoder. Both the input and output of the
spiking neural model are spike information, which is a brain-inspired
intelligence approach that can be better integrated with BCI in the future.
