Stanford Institute for Neuroinnovation and Translational Neuroscience, W100-A, James H Clark Center, Stanford University, Stanford, CA 94305-5436, USA.
J Neural Eng. 2013 Apr;10(2):026002. doi: 10.1088/1741-2560/10/2/026002. Epub 2013 Jan 31.
Brain-machine interface systems translate recorded neural signals into command signals for assistive technology. In individuals with upper limb amputation or cervical spinal cord injury, the restoration of a useful hand grasp could significantly improve daily function. We sought to determine if electrocorticographic (ECoG) signals contain sufficient information to select among multiple hand postures for a prosthetic hand, orthotic, or functional electrical stimulation system.
We recorded ECoG signals from subdural macro- and microelectrodes implanted in motor areas of three participants who were undergoing inpatient monitoring for diagnosis and treatment of intractable epilepsy. Participants performed five distinct isometric hand postures, as well as four distinct finger movements. Several control experiments were attempted in order to remove sensory information from the classification results. Online experiments were performed with two participants.
Classification rates were 68%, 84% and 81% for correct identification of 5 isometric hand postures offline. Using 3 potential controls for removing sensory signals, error rates were approximately doubled on average (2.1×). A similar increase in errors (2.6×) was noted when the participant was asked to make simultaneous wrist movements along with the hand postures. In online experiments, fist versus rest was successfully classified on 97% of trials; the classification output drove a prosthetic hand. Online classification performance for a larger number of hand postures remained above chance, but substantially below offline performance. In addition, the long integration windows used would preclude the use of decoded signals for control of a BCI system.
These results suggest that ECoG is a plausible source of command signals for prosthetic grasp selection. Overall, avenues remain for improvement through better electrode designs and placement, better participant training, and characterization of non-stationarities such that ECoG could be a viable signal source for grasp control for amputees or individuals with paralysis.
脑机接口系统将记录的神经信号转换为辅助技术的命令信号。在上肢截肢或颈脊髓损伤患者中，恢复有用的手抓握能力可以显著提高日常生活功能。我们试图确定脑电（ECoG）信号是否包含足够的信息来选择假肢、矫形器或功能性电刺激系统的多个手姿势。
我们记录了三名接受住院监测以诊断和治疗难治性癫痫的参与者的脑区硬膜下宏观和微观电极记录的 ECoG 信号。参与者执行了五个不同的等长手姿势，以及四个不同的手指运动。尝试了几个对照实验以从分类结果中去除感觉信息。两名参与者进行了在线实验。
离线时，正确识别 5 个等长手姿势的分类率分别为 68%、84%和 81%。使用 3 个潜在的控制来去除感觉信号，平均错误率增加了大约两倍（2.1×）。当参与者被要求在手姿势的同时进行手腕运动时，也注意到类似的错误增加（2.6×）。在线实验中，拳头与休息的分类在 97%的试验中成功；分类输出驱动了假肢。在线时，对更多手姿势的分类性能仍然高于机会水平，但大大低于离线性能。此外，使用的长积分窗口将排除使用解码信号来控制 BCI 系统。
这些结果表明，ECoG 是假肢抓握选择的命令信号的合理来源。总体而言，通过更好的电极设计和放置、更好的参与者培训以及非平稳性的表征，仍然有改进的途径，以便 ECoG 可以成为截肢者或瘫痪者抓握控制的可行信号源。
