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

ql20@mails.tsinghua.edu.cn Abstract. Brain-computer interfaces (BCIs), transform neural signals in the brain into instructions 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 neural network and is considered as one of the algorithms oriented to general artificial intelligence because it borrows the neural information processing from biological 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. Experiments show that the model can direct-ly 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 specific patterns of neural population activity rather than on individual neurons[4]. The neural population dynamics exist in low-dimensional neural manifolds in a high-dimensional neural space[5]. Here, we employ a bio-interpretive SNN that mimics the neural information generation as well as the com-munication of biological neural populations. We analyze motor cortical neural population data recorded from monkeys to derive motor-related neural population dynamics. The neural spike properties of the SNN itself allow the direct generation of biologically meaningful spike trains that match the activity of real biological neural populations. We explored the interaction between the spike train synthesizer and the BCI decoder. Our results show that based on a small amount of training data as a template, data conforming to the dynamics of neural populations are generated, thus enhancing the decoding ability of the BCI decoder.