Individual finger control of a modular prosthetic limb using high-density electrocorticography in a human subject.
Individual finger control of a modular prosthetic limb using high-density electrocorticography in a human subject.
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DOI：
10.1088/1741-2560/13/2/026017
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发表时间：
2016-04
影响因子：
4
通讯作者：
Crone NE
中科院分区：
文献类型：
--
作者：
Hotson G;McMullen DP;Fifer MS;Johannes MS;Katyal KD;Para MP;Armiger R;Anderson WS;Thakor NV;Wester BA;Crone NE
我们在脑机接口中使用手指的原生感觉运动表征来实现对单个假肢手指的即时在线控制。使用高伽马响应记录与高密度ECoG阵列，我们迅速映射的功能解剖提示手指运动。我们使用这些皮层图来选择ECoG电极用于分层线性判别分析分类方案，以预测：1）是否有任何手指在移动，如果是，2）哪个手指在移动。为了解释感觉反馈，我们还绘制了由振动触觉刺激引起的时空激活。最后，我们使用这个预测框架来提供对约翰霍普金斯大学应用物理实验室（JHU/APL）模块化假肢（MPL）的单个手指的即时在线控制。在线控制会话期间检测运动的平衡分类准确率为92%（机会：50%）。在运动开始时，手指分类为76%（机会：20%），88%（机会：25%），如果小指和无名指运动耦合。完全弯曲提示手指的平衡准确率为64%，77%的人结合了小指和戒指命令。当使用优化的电极选择时，离线解码产生了96.5%的峰值手指解码准确度（机会：20%）。离线分析表明，整个感觉运动皮层显着的手指特异性激活。运动开始之前或感觉反馈期间的激活导致可辨别的手指控制。我们的研究结果表明，基于ECoG的BMI能够利用感觉运动皮层人群的本地功能解剖结构，立即控制真实的时间的个人手指运动。
We used native sensorimotor representations of fingers in a brain-machine interface to achieve immediate online control of individual prosthetic fingers. Using high gamma responses recorded with a high-density ECoG array, we rapidly mapped the functional anatomy of cued finger movements. We used these cortical maps to select ECoG electrodes for a hierarchical linear discriminant analysis classification scheme to predict: 1) if any finger was moving, and, if so, 2) which digit was moving. To account for sensory feedback, we also mapped the spatiotemporal activation elicited by vibrotactile stimulation. Finally, we used this prediction framework to provide immediate online control over individual fingers of the Johns Hopkins University Applied Physics Laboratory (JHU/APL) Modular Prosthetic Limb (MPL). The balanced classification accuracy for detection of movements during the online control session was 92% (chance: 50%). At the onset of movement, finger classification was 76% (chance: 20%), and 88% (chance: 25%) if the pinky and ring finger movements were coupled. Balanced accuracy of fully flexing the cued finger was 64%, and 77% had we combined pinky and ring commands. Offline decoding yielded a peak finger decoding accuracy of 96.5% (chance: 20%) when using an optimized selection of electrodes. Offline analysis demonstrated significant finger-specific activations throughout sensorimotor cortex. Activations either prior to movement onset or during sensory feedback led to discriminable finger control. Our results demonstrate the ability of ECoG-based BMIs to leverage the native functional anatomy of sensorimotor cortical populations to immediately control individual finger movements in real time.