Faith Aaron, Chen Yinpeng, Rikakis Thanassis, Iasemidis Leonidas
School of Biological and Health Systems Engineering, Harrington Biomedical Engineering and the School of Arts, Media and Engineering, Arizona State University, Tempe, AZ 85287, USA.
Annu Int Conf IEEE Eng Med Biol Soc. 2011;2011:1387-90. doi: 10.1109/IEMBS.2011.6090326.
Electroencephalography (EEG) has been used for decades to measure the brain's electrical activity. Planning and performing a complex movement (e.g., reaching and grasping) requires the coordination of muscles by electrical activity that can be recorded with scalp EEG from relevant regions of the cortex. Prior studies, utilizing motion capture and kinematic measures, have shown that an augmented reality feedback system for rehabilitation of stroke patients can help patients develop new motor plans and perform reaching tasks more accurately. Historically, traditional signal analysis techniques have been utilized to quantify changes in EEG when subjects perform common, simple movements. These techniques have included measures of event-related potentials in the time and frequency domains (e.g., energy and coherence measures). In this study, a more advanced, nonlinear, analysis technique, mutual information (MI), is applied to the EEG to capture the dynamics of functional connections between brain sites. In particular, the cortical activity that results from the planning and execution of novel reach trajectories by normal subjects in an augmented reality system was quantified by using statistically significant MI interactions between brain sites over time. The results show that, during the preparation for as well as the execution of a reach, the functional connectivity of the brain changes in a consistent manner over time, in terms of both the number and strength of cortical connections. A similar analysis of EEG from stroke patients may provide new insights into the functional deficiencies developed in the brain after stroke, and contribute to evaluation, and possibly the design, of novel therapeutic schemes within the framework of rehabilitation and BMI (brain machine interface).
脑电图（EEG）已被用于测量大脑电活动数十年。规划和执行复杂动作（如伸手抓取）需要通过电活动来协调肌肉，而这种电活动可以通过头皮脑电图从皮层的相关区域记录下来。先前利用动作捕捉和运动学测量的研究表明，用于中风患者康复的增强现实反馈系统可以帮助患者制定新的运动计划并更准确地执行伸手任务。从历史上看，传统信号分析技术已被用于量化受试者执行常见简单动作时脑电图的变化。这些技术包括时域和频域中与事件相关电位的测量（如能量和相干性测量）。在本研究中，一种更先进的非线性分析技术——互信息（MI），被应用于脑电图，以捕捉脑区之间功能连接的动态变化。具体而言，通过使用脑区之间随时间具有统计学显著性的MI相互作用，对正常受试者在增强现实系统中规划和执行新的伸手轨迹时产生的皮层活动进行了量化。结果表明，在伸手动作的准备和执行过程中，大脑的功能连接性会随着时间的推移在皮层连接的数量和强度方面以一致的方式发生变化。对中风患者脑电图进行类似分析可能会为中风后大脑出现的功能缺陷提供新的见解，并有助于在康复和脑机接口（BMI）框架内评估甚至设计新的治疗方案。
