Abstract：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 relationship between the internal mechanism of attention and behavioral performance is still worth exploring. In this paper, the data of different degrees of attention were obtained by controlling the difficulty of the rapid serial visual presentation task (30 subjects). The directed information transfer function was used to analyze the network information characteristics of the attention state. Subsequently, the neurofeedback training method was designed with reference to these features to improve attention and the using skill of SSVEP-based brain-computer interface. The results of the connection strength between the leads showed that the prefrontal lobe, parietal lobe and occipital lobe constituted a joint processing system in the visual task. In this system, the parietal lobe played the role of central control, and alpha oscillation participates in the modulation process of attention. Thus, 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. After this NFT, the signal-to-noise ratio (SNR), accuracy, and information transfer rate (ITR) of SSVEP-based BCI were (-15.23±5.91) dB and (-14.72±4.83) dB; 78.93%±0.14% and 83.65%±0.14%; 103.21±28.49 and 119.35±25.14 respectively (24 subjects). However, no improvement has been observed in the control group (22 subjects) in which the subjects do not participate in NFT. What's more, evidence from attention test further indicate that NFT improves attention via developing the control ability of the parietal lobe and then enhances the above SSVEP indicators. Up-regulating parietal alpha-amplitude using neurofeedback training significantly improves the SSVEP-based BCI performance through modulating the control network. In conclusion, this study validated an effective neuromodulation method, and possibly also contributed to explaining the function of the parietal lobe in the control network.
孙劲男, 张山根, 高小榕. 神经反馈提升注意及SSVEP脑机接口使用技能[J]. 中国生物医学工程学报, 2021, 40(1): 33-43.
Sun Jinnan, Zhang Shangen, Gao Xiaorong. Research on Using Neurofeedback to Improve Attention and Skills of SSVEP Brain-Computer Interface. Chinese Journal of Biomedical Engineering, 2021, 40(1): 33-43.
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