Department of Physical Medicine and Rehabilitation, University of Pittsburgh, Pittsburgh, Pennsylvania.
Department of Bioengineering, University of Pittsburgh, Pittsburgh, Pennsylvania.
Neurosurgery. 2020 Sep 15;87(4):630-638. doi: 10.1093/neuros/nyaa026.
Intracortical microelectrode arrays have enabled people with tetraplegia to use a brain-computer interface for reaching and grasping. In order to restore dexterous movements, it will be necessary to control individual fingers.
To predict which finger a participant with hand paralysis was attempting to move using intracortical data recorded from the motor cortex.
A 31-yr-old man with a C5/6 ASIA B spinal cord injury was implanted with 2 88-channel microelectrode arrays in left motor cortex. Across 3 d, the participant observed a virtual hand flex in each finger while neural firing rates were recorded. A 6-class linear discriminant analysis (LDA) classifier, with 10 × 10-fold cross-validation, was used to predict which finger movement was being performed (flexion/extension of all 5 digits and adduction/abduction of the thumb).
The mean overall classification accuracy was 67% (range: 65%-76%, chance: 17%), which occurred at an average of 560 ms (range: 420-780 ms) after movement onset. Individually, thumb flexion and thumb adduction were classified with the highest accuracies at 92% and 93%, respectively. The index, middle, ring, and little achieved an accuracy of 65%, 59%, 43%, and 56%, respectively, and, when incorrectly classified, were typically marked as an adjacent finger. The classification accuracies were reflected in a low-dimensional projection of the neural data into LDA space, where the thumb-related movements were most separable from the finger movements.
Classification of intention to move individual fingers was accurately predicted by intracortical recordings from a human participant with the thumb being particularly independent.
皮层内微电极阵列使四肢瘫痪的人能够使用脑机接口进行伸手和抓握。为了恢复灵巧的动作，将有必要控制单个手指。
使用从运动皮层记录的皮层内数据来预测手部瘫痪的参与者试图移动哪个手指。
一名 31 岁的 C5/6 ASIA B 脊髓损伤患者在左运动皮层植入了 2 个 88 通道微电极阵列。在 3 天内，参与者观察到虚拟手在每个手指弯曲时，记录神经发射率。使用 6 类线性判别分析（LDA）分类器（10×10 折交叉验证）预测正在进行的手指运动（所有 5 个手指的弯曲/伸展和拇指的内收/外展）。
总体平均分类准确率为 67%（范围：65%-76%，机会：17%），平均发生在运动开始后 560 毫秒（范围：420-780 毫秒）。单独地，拇指弯曲和拇指内收的分类准确率分别高达 92%和 93%。索引、中指、环指和小指的准确率分别为 65%、59%、43%和 56%，当分类错误时，通常标记为相邻手指。分类准确率反映在将神经数据低维投影到 LDA 空间中，拇指相关运动与手指运动最可分离。
通过对一名人类参与者的皮层内记录，准确预测了移动单个手指的意图分类，拇指特别独立。
