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脑电信号振幅可预测手进行缓慢抓握运动时的手指位置。

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

机构信息

Department of Biomedical Engineering, Johns Hopkins University School of Medicine, Baltimore, MD 21205, USA.

出版信息

J Neural Eng. 2010 Aug;7(4):046002. doi: 10.1088/1741-2560/7/4/046002. Epub 2010 May 20.

Abstract

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.

摘要

四位接受癫痫手术的硬膜下脑电描记术的人类受试者进行了一系列手指和手部运动。我们观察到,在特定的 Roland 周围电极上,经低通滤波的脑电描记图(ECoG),也称为局部运动电位(LMP)的幅度与受试者进行缓慢而有意的抓握运动时的单个手指位置相关(p < 0.001)。这些电极的 LMP 幅度的广义线性模型(GLM)产生了对手指位置的预测,这些预测与实际手指位置具有很强的一致性(相关系数 r;中位数= 0.51,最大值= 0.91),在指尖最大可达 10 cm 的位移范围内。对于所有受试者,从任何给定会话的数据训练的解码滤波器在多个会话和多天内的预测性能都非常稳健,并且对于这些会话中手腕角度、肘部弯曲和手部位置的变化具有不变性(中位数 r = 0.52,最大值 r = 0.86)。此外,在所有受试者中,仅使用三个电极就可以实现合理的抓握开口预测精度(中位数 r = 0.49;最大值 r = 0.90)。这些结果进一步证明了基于 ECoG 的手指运动控制在上肢假肢中具有稳健和实用的可行性。

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