Neurofeedback training of control network improves SSVEP-based BCI performance in children

Abstract Background: In the past 20 years, neural engineering has made unprecedented progress in the interpretation of brain information (e.g., brain-computer interfaces) and neuromodulation (e.g., electromagnetic stimulation and neurofeedback). However, the study of improving the performance of the brain-computer interface (BCI) using the neuromodulation method rarely exists. The present study designs a neurofeedback training method to improve the performance of steady-state visual evoked potential (SSVEP) BCI and further explores its underlying mechanisms. Methods: As parietal lobe is the sole hub of information transmission, up-regulating alpha-band power of the parietal lobe by neurofeedback training was present as a new neural modulating method to improve SSVEP-based BCI in this study. Results: After this neurofeedback training (NFT), the signal-to-noise ratio (SNR), accuracy, and information transfer rate (ITR) of SSVEP-based BCI were increased by 5.8%, 4.7%, and 15.6% respectively. However, no improvement has been observed in the control group in which the subjects do not participate in NFT. What’s more, a general reinforcement of the information flow from the parietal lobe to the occipital lobe was also observed. Evidence from the network analysis and attention test further indicate that NFT improves attention via developing the control ability of the parietal lobe and then enhances the above SSVEP indicators. Conclusion: Up-regulating parietal alpha-amplitude using neurofeedback training significantly improves the SSVEP-baesd BCI performance through modulating the control network. The study validates an effective neuromodulation method, and possibly also contributes to explaining the function of the parietal lobe in the control network.