fNIRS-GANs: data augmentation using generative adversarial networks for classifying motor tasks from functional near-infrared spectroscopy.

OBJECTIVE: Functional near-infrared spectroscopy (fNIRS) is expected to be applied to brain-computer interface (BCI) technologies. Since lengthy fNIRS measurements are uncomfortable for participants, it is difficult to obtain enough data to train classification models; hence, the fNIRS-BCI accuracy decreases. APPROACH: In this study, to improve the fNIRS-BCI accuracy, we examined an fNIRS data augmentation method using Wasserstein generative adversarial networks (WGANs). Using fNIRS data during hand-grasping tasks, we evaluated whether the proposed data augmentation method could generate artificial fNIRS data and improve the classification performance using support vector machines and simple neural networks. MAIN
RESULTS: Trial-averaged temporal profiles of WGAN-generated fNIRS data were similar to those of the measured data except that they contained an extra noise component. By augmenting the generated data to training data, the accuracies for classifying four different task types were improved irrespective of the classifiers. SIGNIFICANCE: This result suggests that the artificial fNIRS data generated by the proposed data augmentation method is useful for improving BCI performance.