Rethinking Delayed Hemodynamic Responses for fNIRS Classification.

Abstract

Functional near-infrared spectroscopy (fNIRS) is a non-invasive neuroimaging technology for monitoring cerebral hemodynamic responses. Enhancing fNIRS classification can improve the performance of brain-computer interfaces (BCIs). Currently, deep neural networks (DNNs) do not consider the inherent delayed hemodynamic responses of fNIRS signals, which causes many optimization and application problems. Considering the kernel size and receptive field of convolutions, delayed hemodynamic responses as domain knowledge are introduced into fNIRS classification, and a concise and efficient model named fNIRSNet is proposed. We empirically summarize three design guidelines for fNIRSNet. In subject-specific and subject-independent experiments, fNIRSNet outperforms other DNNs on open-access datasets. Specifically, fNIRSNet with only 498 parameters is 6.58% higher than convolutional neural network (CNN) with millions of parameters on mental arithmetic tasks and the floating-point operations (FLOPs) of fNIRSNet are much lower than CNN. Therefore, fNIRSNet is friendly to practical applications and reduces the hardware cost of BCI systems. It may inspire more research on knowledge-driven models for fNIRS BCIs. Code is available at https://github.com/wzhlearning/fNIRSNet.