1Department of Biomedical Engineering, New Jersey Institute of Technology, Newark, New Jersey 07102, USA.
Int J Neural Syst. 2019 Sep;29(7):1950009. doi: 10.1142/S0129065719500096. Epub 2019 Feb 28.
Brain-computer interfaces access the volitional command signals from various brain areas in order to substitute for the motor functions lost due to spinal cord injury or disease. As the final common pathway of the central nervous system (CNS) outputs, the descending tracts of the spinal cord offer an alternative site to extract movement-related command signals. Using flexible 2D microelectrode arrays, we have recorded the corticospinal tract (CST) signals in rats during a reach-to-pull task. The CST activity was then classified by the forelimb movement phases into two or three classes in a training dataset and cross validated in a test set. The average classification accuracies were (min: to max: ) and (min: 43% to max: 71%) for two-class and three-class cases, respectively. The forelimb flexor and extensor EMG envelopes were also predicted from the CST signals using linear regression. The average correlation coefficient between the actual and predicted EMG signals was , whereas the highest correlation was 0.81 for the biceps EMG. Although the forelimb motor function cannot be explained completely by the CST activity alone, the success rates obtained in reconstructing the EMG signals support the feasibility of a spinal-cord-computer interface as a concept.
脑机接口从各种脑区获取随意的命令信号，以替代因脊髓损伤或疾病而丧失的运动功能。作为中枢神经系统（CNS）输出的最终共同途径，脊髓的下行束为提取与运动相关的命令信号提供了替代部位。我们使用灵活的 2D 微电极阵列，在大鼠进行抓握-拉动任务时记录皮质脊髓束（CST）信号。然后，将 CST 活动根据前肢运动阶段分类为训练数据集中的两个或三个类别，并在测试集中进行交叉验证。对于两类和三类情况，平均分类准确率分别为 （最小值： 至最大值： ）和 （最小值：43%至最大值：71%）。还使用线性回归从 CST 信号预测前肢屈肌和伸肌肌电图包络。实际肌电图信号和预测肌电图信号之间的平均相关系数为 ，而肱二头肌肌电图的最高相关系数为 0.81。尽管前肢运动功能不能仅通过 CST 活动完全解释，但在重建肌电图信号方面获得的成功率支持了脊髓-计算机接口作为一种概念的可行性。
