Battapady Harsha, Lin Peter, Holroyd Tom, Hallett Mark, Chen Xuedong, Fei Ding-Yu, Bai Ou
EEG & BCI Laboratory, Department of Biomedical Engineering, Virginia Commonwealth University, Richmond, VA 23284, USA.
Human Motor Control Section, Medical Neurological Branch, National Institute of Neurological Disorders and Stroke, National Institutes of Health, Bethesda, MD 20892, USA.
Clin Neurophysiol. 2009 Nov;120(11):1978-1987. doi: 10.1016/j.clinph.2009.08.017. Epub 2009 Sep 24.
To test whether human intentions to sustain or cease movements in right and left hands can be decoded reliably from spatially filtered single-trial magnetoencephalographic (MEG) signals for motor execution and motor imagery.
Seven healthy volunteers, naïve to BCI technology, participated in this study. Signals were recorded from 275-channel MEG, and synthetic aperture magnetometry (SAM) was employed as the spatial filter. The four-class classification was performed offline. Genetic algorithm based Mahalanobis linear distance (GA-MLD) and direct-decision tree classifier (DTC) techniques were adopted for the classification through 10-fold cross-validation.
Through SAM imaging, strong and distinct event-related desynchronization (ERD) associated with sustaining, and event-related synchronization (ERS) patterns associated with ceasing of right and left hand movements were observed in the beta band (15-30Hz) on the contralateral hemispheres for motor execution and motor imagery sessions. Virtual channels were selected from these areas of high activity for the corresponding events as per the paradigm of the study. Through a statistical comparison between SAM-filtered virtual channels from single-trial MEG signals and basic MEG sensors, it was found that SAM-filtered virtual channels significantly increased the classification accuracy for motor execution (GA-MLD: 96.51+/-2.43%) as well as motor imagery sessions (GA-MLD: 89.69+/-3.34%).
Multiple movement intentions can be reliably detected from SAM-based spatially filtered single-trial MEG signals.
MEG signals associated with natural motor behavior may be utilized for a reliable high-performance brain-computer interface (BCI) and may reduce long-term training compared with conventional BCI methods using rhythm control.
测试能否从用于运动执行和运动想象的空间滤波单通道脑磁图（MEG）信号中可靠地解码出人类右手和左手维持或停止运动的意图。
七名对脑机接口技术不了解的健康志愿者参与了本研究。信号由275通道MEG记录，并采用合成孔径磁ometry（SAM）作为空间滤波器。离线进行四类分类。采用基于遗传算法的马氏线性距离（GA-MLD）和直接决策树分类器（DTC）技术，通过10倍交叉验证进行分类。
通过SAM成像，在运动执行和运动想象过程中，对侧半球的β波段（15-30Hz）观察到与右手和左手运动维持相关的强烈且明显的事件相关去同步化（ERD），以及与停止相关的事件相关同步化（ERS）模式。根据研究范式，从这些对应事件的高活动区域中选择虚拟通道。通过对单通道MEG信号的SAM滤波虚拟通道与基本MEG传感器进行统计比较，发现SAM滤波虚拟通道显著提高了运动执行（GA-MLD：96.51±2.43%）以及运动想象过程（GA-MLD：89.69±3.34%）的分类准确率。
可以从基于SAM的空间滤波单通道MEG信号中可靠地检测到多种运动意图。
与使用节律控制的传统脑机接口方法相比，与自然运动行为相关的MEG信号可用于可靠的高性能脑机接口（BCI），并可能减少长期训练。
