Department of Biomedical Engineering, University of Minnesota, MN, USA.
J Neural Eng. 2010 Apr;7(2):26001. doi: 10.1088/1741-2560/7/2/026001. Epub 2010 Feb 18.
The relationship between primary motor cortex and movement kinematics has been shown in nonhuman primate studies of hand reaching or drawing tasks. Studies have demonstrated that the neural activities accompanying or immediately preceding the movement encode the direction, speed and other information. Here we investigated the relationship between the kinematics of imagined and actual hand movement, i.e. the clenching speed, and the EEG activity in ten human subjects. Study participants were asked to perform and imagine clenching of the left hand and right hand at various speeds. The EEG activity in the alpha (8-12 Hz) and beta (18-28 Hz) frequency bands were found to be linearly correlated with the speed of imagery clenching. Similar parametric modulation was also found during the execution of hand movements. A single equation relating the EEG activity to the speed and the hand (left versus right) was developed. This equation, which contained a linear independent combination of the two parameters, described the time-varying neural activity during the tasks. Based on the model, a regression approach was developed to decode the two parameters from the multiple-channel EEG signals. We demonstrated the continuous decoding of dynamic hand and speed information of the imagined clenching. In particular, the time-varying clenching speed was reconstructed in a bell-shaped profile. Our findings suggest an application to providing continuous and complex control of noninvasive brain-computer interface for movement-impaired paralytics.
在非人类灵长类动物对手部伸展或绘图任务的研究中,已经证明了初级运动皮层与运动运动学之间的关系。研究表明,伴随运动或在运动之前发生的神经活动编码了方向、速度和其他信息。在这里,我们研究了想象和实际手部运动(即握拳速度)之间的运动学关系,以及十名人类受试者的脑电图活动。研究参与者被要求以各种速度执行和想象左手和右手的握拳。发现 alpha(8-12 Hz)和 beta(18-28 Hz)频段的脑电图活动与想象握拳的速度呈线性相关。在手部运动执行过程中也发现了类似的参数调制。开发了一个将脑电图活动与速度和手(左手与右手)相关联的单一方程。这个方程包含了这两个参数的线性独立组合,描述了任务期间的时变神经活动。基于该模型,开发了一种回归方法,从多通道脑电图信号中解码这两个参数。我们演示了从想象中的握拳的动态手部和速度信息的连续解码。特别是,重建了时变握拳速度的钟形轮廓。我们的发现表明,它可以应用于为运动障碍的瘫痪患者提供连续和复杂的非侵入性脑机接口控制。