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Brain-computer interfaces (BCIs), transform neural signals in the brain intoin-structions to control external devices. However, obtaining sufficienttraining data is difficult as well as limited. With the advent of advancedmachine learning methods, the capability of brain-computer interfaces has beenenhanced like never before, however, these methods require a large amount ofdata for training and thus require data augmentation of the limited dataavailable. Here, we use spiking neural networks (SNN) as data generators. It istouted as the next-generation neu-ral network and is considered as one of thealgorithms oriented to general artifi-cial intelligence because it borrows theneural information processing from bio-logical neurons. We use the SNN togenerate neural spike information that is bio-interpretable and conforms to theintrinsic patterns in the original neural data. Ex-periments show that themodel can directly synthesize new spike trains, which in turn improves thegeneralization ability of the BCI decoder. Both the input and output of thespiking neural model are spike information, which is a brain-inspiredintelligence approach that can be better integrated with BCI in the future.

A Spiking Neural Network based on Neural Manifold for Augmenting Intracortical Brain-Computer Interface Data