Electrocorticographic amplitude predicts finger positions during slow grasping motions of the hand.

Electrocorticographic amplitude predicts finger positions during slow grasping motions of the hand.
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DOI:
10.1088/1741-2560/7/4/046002
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发表时间:
2010-08
影响因子:
4
通讯作者:
Thakor NV
Thakor NV
中科院分区:
工程技术2区
文献类型:
--
作者:
Acharya S;Fifer MS;Benz HL;Crone NE;Thakor NV

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接受癫痫手术的四名人类受试者进行了一系列的手指和手的运动。我们观察到,在特定的罗兰迪克周围电极上的低通滤波皮层脑电(ECoG),也称为局部运动电位(LMP)的幅度与受试者在进行缓慢和有意的抓握运动时的单个手指的位置相关(p<0.001)。这些电极的LMP幅度的广义线性模型(GLM)预测了在指尖位移达10厘米期间与实际手指位置有很强一致性的手指位置(相关系数r;中位数=0.51,最大值=0.91)。对于所有受试者,根据任何给定会话的数据训练的解码过滤器在多个会话和日期中的预测性能非常稳健,并且对于这些会话中手腕角度、肘关节屈曲和手的位置的变化不变(中位数r=0.52,最大r=0.86)。此外,在所有受试者中,只需三个电极即可获得合理的抓握孔径预测精度(中位数r=0.49;最大r=0.90)。这些结果进一步证明了基于皮层脑电的手指运动控制在上肢假肢中的可行性。
Four human subjects undergoing subdural electrocorticography for epilepsy surgery engaged in a range of finger and hand movements. We observed that the amplitudes of the low-pass filtered electrocorticogram (ECoG), also known as the local motor potential (LMP), over specific peri-Rolandic electrodes were correlated (p < 0.001) with the position of individual fingers as the subjects engaged in slow and deliberate grasping motions. A generalized linear model (GLM) of the LMP amplitudes from those electrodes yielded predictions for positions of the fingers that had a strong congruence with the actual finger positions (correlation coefficient, r; median = 0.51, maximum = 0.91), during displacements of up to 10 cm at the fingertips. For all the subjects, decoding filters trained on data from any given session were remarkably robust in their prediction performance across multiple sessions and days, and were invariant with respect to changes in wrist angle, elbow flexion and hand placement across these sessions (median r = 0.52, maximum r = 0.86). Furthermore, a reasonable prediction accuracy for grasp aperture was achievable with as few as three electrodes in all subjects (median r = 0.49; maximum r = 0.90). These results provide further evidence for the feasibility of robust and practical ECoG-based control of finger movements in upper extremity prosthetics.
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