Zhou Qing, Cheng Ruidong, Yao Lin, Ye Xiangming, Xu Kedi
Zhejiang Lab, Hangzhou, China.
Qiushi Academy for Advanced Studies (QAAS), Zhejiang University, Hangzhou, China.
Front Hum Neurosci. 2022 Apr 8;16:831995. doi: 10.3389/fnhum.2022.831995. eCollection 2022.
Significant variation in performance in motor imagery (MI) tasks impedes their wide adoption for brain-computer interface (BCI) applications. Previous researchers have found that resting-state alpha-band power is positively correlated with MI-BCI performance. In this study, we designed a neurofeedback training (NFT) protocol based on the up-regulation of the alpha band relative power (RP) to investigate its effect on MI-BCI performance. The principal finding of this study is that alpha NFT could successfully help subjects increase alpha-rhythm power and improve their MI-BCI performance. An individual difference was also found in this study in that subjects who increased alpha power more had a better performance improvement. Additionally, the functional connectivity (FC) of the frontal-parietal (FP) network was found to be enhanced after alpha NFT. However, the enhancement failed to reach a significant level after multiple comparisons correction. These findings contribute to a better understanding of the neurophysiological mechanism of cognitive control through alpha regulation.
运动想象(MI)任务表现的显著差异阻碍了其在脑机接口(BCI)应用中的广泛应用。先前的研究人员发现,静息状态下的阿尔法波段功率与MI-BCI表现呈正相关。在本研究中,我们基于阿尔法波段相对功率(RP)的上调设计了一种神经反馈训练(NFT)方案,以研究其对MI-BCI表现的影响。本研究的主要发现是,阿尔法NFT可以成功帮助受试者增加阿尔法节律功率并改善其MI-BCI表现。本研究还发现了个体差异,即阿尔法功率增加更多的受试者表现改善更好。此外,发现阿尔法NFT后额顶叶(FP)网络的功能连接(FC)增强。然而,经过多重比较校正后,这种增强未达到显著水平。这些发现有助于更好地理解通过阿尔法调节进行认知控制的神经生理机制。