Wang Kun, Tian Feifan, Xu Minpeng, Zhang Shanshan, Xu Lichao, Ming Dong
Tianjin International Joint Research Center for Neural Engineering, Academy of Medical Engineering and Translational Medicine, Tianjin University, Tianjin 300072, China.
Department of Biomedical Engineering, College of Precision Instruments and Optoelectronics Engineering, Tianjin University, Tianjin 300072, China.
Entropy (Basel). 2022 Oct 29;24(11):1556. doi: 10.3390/e24111556.
Motor imagery-based brain-computer interfaces (MI-BCIs) have great application prospects in motor enhancement and rehabilitation. However, the capacity to control a MI-BCI varies among persons. Predicting the MI ability of a user remains challenging in BCI studies. We first calculated the relative power level (), power spectral entropy () and Lempel-Ziv complexity () of the resting-state open and closed-eye EEG of different frequency bands and investigated their correlations with the upper and lower limbs MI performance (left hand, right hand, both hands and feet MI tasks) on as many as 105 subjects. Then, the most significant related features were used to construct a classifier to separate the high MI performance group from the low MI performance group. The results showed that the features of open-eye resting alpha-band EEG had the strongest significant correlations with MI performance. The performed the best among all features for the screening of the MI performance, with the classification accuracy of 85.24%. These findings demonstrated that the alpha bands might offer information related to the user's MI ability, which could be used to explore more effective and general neural markers to screen subjects and design individual MI training strategies.
基于运动想象的脑机接口（MI-BCIs）在运动增强和康复方面具有巨大的应用前景。然而，不同个体控制MI-BCI的能力存在差异。在脑机接口研究中，预测用户的运动想象能力仍然具有挑战性。我们首先计算了105名受试者不同频段静息状态下睁眼和闭眼脑电图的相对功率水平（）、功率谱熵（）和Lempel-Ziv复杂度（），并研究了它们与上肢和下肢运动想象表现（左手、右手、双手和双脚运动想象任务）的相关性。然后，使用最显著的相关特征构建分类器，将高运动想象表现组与低运动想象表现组区分开来。结果表明，睁眼静息α波段脑电图的特征与运动想象表现的相关性最强。在所有用于筛选运动想象表现的特征中，表现最佳，分类准确率为85.24%。这些发现表明，α波段可能提供与用户运动想象能力相关的信息，可用于探索更有效和通用的神经标记物，以筛选受试者并设计个性化的运动想象训练策略。
