A Brain-Machine Interface Enables Bimanual Arm Movements in Monkeys
A Brain-Machine Interface Enables Bimanual Arm Movements in Monkeys
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DOI：
10.1126/scitranslmed.3006159
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发表时间：
2013-11-06
影响因子：
17.1
通讯作者：
Nicolelis, Miguel A. L.
中科院分区：
文献类型：
--
作者：
Ifft, Peter J.;Shokur, Solaiman;Nicolelis, Miguel A. L.
脑机接口（BMIs）是旨在恢复瘫痪患者感觉和运动的人工系统。到目前为止，BMI一次只能移动一个手臂。双手手臂运动的控制仍然是一个重大挑战。我们已经开发并测试了一种双手BMI，使恒河猴能够同时控制两个化身手臂。双手BMI是基于从两个大脑半球的几个额叶和顶叶皮质区记录的374至497个神经元的细胞外活动。大脑皮层活动被转换成双臂的运动，解码算法称为五阶无迹卡尔曼滤波器（UKF）。UKF的训练要么是在用两个摇杆执行手动任务的过程中，要么是让猴子被动地观察化身手臂的运动。大多数皮层神经元改变了他们的调制模式时，双臂同时从事。代表两个手臂联合在一个单一的UKF解码器导致改善解码性能相比，使用单独的解码器为每个arm.As动物的表现在双手BMI控制改善随着时间的推移，我们观察到广泛的可塑性在额叶和顶叶皮质区。通过学习，化身和到达目标的神经元表示得到增强，而神经元之间的成对相关性最初增加，然后减少。这些结果表明，皮质网络可能通过BMI控制同化两个化身手臂。这些发现应该有助于设计更复杂的BMI，能够使人类患者的双手运动控制。
Brain-machine interfaces (BMIs) are artificial systems that aim to restore sensation and movement to paralyzed patients. So far, BMIs have enabled only one arm to be moved at a time. Control of bimanual arm movements remains a major challenge. We have developed and tested a bimanual BMI that enables rhesus monkeys to control two avatar arms simultaneously. The bimanual BMI was based on the extracellular activity of 374 to 497 neurons recorded from several frontal and parietal cortical areas of both cerebral hemispheres. Cortical activity was transformed into movements of the two arms with a decoding algorithm called a fifth-order unscented Kalman filter (UKF). The UKF was trained either during a manual task performed with two joysticks or by having the monkeys passively observe the movements of avatar arms. Most cortical neurons changed their modulation patterns when both arms were engaged simultaneously. Representing the two arms jointly in a single UKF decoder resulted in improved decoding performance compared with using separate decoders for each arm. As the animals' performance in bimanual BMI control improved over time, we observed widespread plasticity in frontal and parietal cortical areas. Neuronal representation of the avatar and reach targets was enhanced with learning, whereas pairwise correlations between neurons initially increased and then decreased. These results suggest that cortical networks may assimilate the two avatar arms through BMI control. These findings should help in the design of more sophisticated BMIs capable of enabling bimanual motor control in human patients.