Talakoub Omid, Popovic Milos R, Navaro Jessie, Hamani Clement, Fonoff Erich T, Wong Willy
Department of Electrical and Computer Engineering, University of Toronto Toronto, ON, Canada ; Institute of Biomaterials and Biomedical Engineering, University of Toronto Toronto, ON, Canada.
Institute of Biomaterials and Biomedical Engineering, University of Toronto Toronto, ON, Canada ; Rehabilitation Engineering Laboratory, Toronto Rehabilitation Institute, University Health Network Toronto, ON, Canada.
Front Neurosci. 2015 Jan 13;8:431. doi: 10.3389/fnins.2014.00431. eCollection 2014.
The detection of movement-related components of the brain activity is useful in the design of brain-machine interfaces. A common approach is to classify the brain activity into a number of templates or states. To find these templates, the neural responses are averaged over each movement task. For averaging to be effective, one must assume that the neural components occur at identical times over repeated trials. However, complex arm movements such as reaching and grasping are prone to cross-trial variability due to the way movements are performed. Typically initiation time, duration of movement and movement speed are variable even as a subject tries to reproduce the same task identically across trials. Therefore, movement-related neural activity will tend to occur at different times across the trials. Due to this mismatch, the averaging of neural activity will not bring into salience movement-related components. To address this problem, we present a method of alignment that accounts for the variabilities in the way the movements are conducted. In this study, arm speed was used to align neural activity. Four subjects had electrocorticographic (ECoG) electrodes implanted over their primary motor cortex and were asked to perform reaching and retrieving tasks using the upper limb contralateral to the site of electrode implantation. The arm speeds were aligned using a non-linear transformation of the temporal axes resulting in average spectrograms with superior visualization of movement-related neural activity when compared to averaging without alignment.
大脑活动中与运动相关成分的检测对于脑机接口的设计很有用。一种常见的方法是将大脑活动分类为多个模板或状态。为了找到这些模板，会在每个运动任务上对神经反应进行平均。为了使平均有效，必须假设神经成分在重复试验中出现在相同的时间。然而，诸如伸手和抓握等复杂的手臂运动由于执行运动的方式而容易出现跨试验变异性。通常，即使受试者试图在不同试验中完全重复相同的任务，起始时间、运动持续时间和运动速度也是可变的。因此，与运动相关的神经活动在不同试验中往往会在不同时间出现。由于这种不匹配，神经活动的平均不会突出与运动相关的成分。为了解决这个问题，我们提出了一种对齐方法，该方法考虑了运动执行方式的变异性。在这项研究中，使用手臂速度来对齐神经活动。四名受试者在其初级运动皮层上植入了皮层脑电图（ECoG）电极，并被要求使用与电极植入部位对侧的上肢执行伸手和取回任务。通过对时间轴进行非线性变换来对齐手臂速度，与未对齐的平均相比，得到的平均频谱图能更好地显示与运动相关的神经活动。
