Liang Shuang, Choi Kup-Sze, Qin Jing, Pang Wai-Man, Wang Qiong, Heng Pheng-Ann
Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen, Guangdong, China.
School of Nursing, The Hong Kong Polytechnic University, Hong Kong.
Comput Methods Programs Biomed. 2016 Aug;132:63-74. doi: 10.1016/j.cmpb.2016.04.023. Epub 2016 Apr 27.
While research on the brain-computer interface (BCI) has been active in recent years, how to get high-quality electrical brain signals to accurately recognize human intentions for reliable communication and interaction is still a challenging task. The evidence has shown that visually guided motor imagery (MI) can modulate sensorimotor electroencephalographic (EEG) rhythms in humans, but how to design and implement efficient visual guidance during MI in order to produce better event-related desynchronization (ERD) patterns is still unclear. The aim of this paper is to investigate the effect of using object-oriented movements in a virtual environment as visual guidance on the modulation of sensorimotor EEG rhythms generated by hand MI. To improve the classification accuracy on MI, we further propose an algorithm to automatically extract subject-specific optimal frequency and time bands for the discrimination of ERD patterns produced by left and right hand MI. The experimental results show that the average classification accuracy of object-directed scenarios is much better than that of non-object-directed scenarios (76.87% vs. 69.66%). The result of the t-test measuring the difference between them is statistically significant (p = 0.0207). When compared to algorithms based on fixed frequency and time bands, contralateral dominant ERD patterns can be enhanced by using the subject-specific optimal frequency and the time bands obtained by our proposed algorithm. These findings have the potential to improve the efficacy and robustness of MI-based BCI applications.
尽管近年来对脑机接口（BCI）的研究十分活跃，但如何获取高质量的脑电信号以准确识别人类意图，从而实现可靠的通信和交互，仍然是一项具有挑战性的任务。有证据表明，视觉引导的运动想象（MI）可以调节人类的感觉运动脑电图（EEG）节律，但如何在运动想象期间设计和实施有效的视觉引导，以产生更好的事件相关去同步化（ERD）模式仍不明确。本文的目的是研究在虚拟环境中使用面向对象的运动作为视觉引导对手部运动想象产生的感觉运动脑电节律调制的影响。为了提高运动想象的分类准确率，我们进一步提出了一种算法，用于自动提取特定于个体的最佳频率和时间带，以区分左右手运动想象产生的ERD模式。实验结果表明，目标导向场景的平均分类准确率远高于非目标导向场景（76.87%对69.66%）。测量两者之间差异的t检验结果具有统计学意义（p = 0.0207）。与基于固定频率和时间带的算法相比，使用我们提出的算法获得的特定于个体的最佳频率和时间带，可以增强对侧优势ERD模式。这些发现有可能提高基于运动想象的脑机接口应用的有效性和鲁棒性。
