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在皮质病变导致对侧手臂无力的情况下,对同侧伸手动作进行皮质脑电图解码。

Electrocorticographic decoding of ipsilateral reach in the setting of contralateral arm weakness from a cortical lesion.

作者信息

Hotson Guy, Fifer Matthew S, Acharya Soumyadipta, Anderson William S, Thakor Nitish V, Crone Nathan E

机构信息

Department of Electrical and Computer Engineering, Johns Hopkins University, Baltimore, MD 21218, USA.

出版信息

Annu Int Conf IEEE Eng Med Biol Soc. 2012;2012:4104-7. doi: 10.1109/EMBC.2012.6346869.

Abstract

Brain machine interfaces have the potential for restoring motor function not only in patients with amputations or lesions of efferent pathways in the spinal cord and peripheral nerves, but also patients with acquired brain lesions such as strokes and tumors. In these patients the most efficient components of cortical motor systems are not available for BMI control. Here we had the opportunity to investigate the possibility of utilizing subdural electrocorticographic (ECoG) signals to control natural reaching movements under these circumstances. In a subject with a left arm monoparesis following resection of a recurrent glioma, we found that ECoG signals recorded in remaining cortex were sufficient for decoding kinematics of natural reach movements of the nonparetic arm, ipsilateral to the ECoG recordings. The relationship between the subject's ECoG signals and reach trajectory in three dimensions, two of which were highly correlated, was captured with a computationally simple linear model (mean Pearson's r in depth dimension= 0.68, in height= 0.73, in lateral= 0.24). These results were attained with only a small subset of 7 temporal/spectral neural signal features. The small subset of neural features necessary to attain high decoding results show promise for a restorative BMI controlled solely by ipsilateral ECoG signals.

摘要

脑机接口不仅有可能恢复截肢患者或脊髓及周围神经传出通路受损患者的运动功能,还能恢复中风和肿瘤等后天性脑损伤患者的运动功能。在这些患者中,皮质运动系统中最有效的组成部分无法用于脑机接口控制。在此,我们有机会研究在这种情况下利用硬膜下皮层脑电图(ECoG)信号控制自然伸展运动的可能性。在一名复发性胶质瘤切除术后出现左臂单瘫的受试者中,我们发现,在剩余皮层记录的ECoG信号足以解码与ECoG记录同侧的非瘫痪手臂自然伸展运动的运动学信息。通过一个计算简单的线性模型(深度维度的平均皮尔逊相关系数r = 0.68,高度维度r = 0.73,横向维度r = 0.24)捕捉到了受试者的ECoG信号与三维伸展轨迹之间的关系,其中两个维度高度相关。这些结果仅通过7个时间/频谱神经信号特征的一个小子集就得以实现。实现高解码结果所需的少量神经特征子集表明,仅通过同侧ECoG信号控制的恢复性脑机接口具有潜力。

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