Neural Engineering Area, The BioRobotics Institute, Scuola Superiore Sant'Anna, Pisa, Italy.
J Neuroeng Rehabil. 2012 Nov 26;9:84. doi: 10.1186/1743-0003-9-84.
In the recent past several invasive cortical neuroprostheses have been developed. Signals recorded from the motor cortex (area MI) have been decoded and used to control computer cursors and robotic devices. Nevertheless, few attempts have been carried out to predict different grips.A Support Vector Machines (SVMs) classifier has been trained for a continuous decoding of four/six grip types using signals recorded in two monkeys from motor neurons of the ventral premotor cortex (area F5) during a reach-to-grasp task.
The results showed that four/six grip types could be extracted with classification accuracy higher than 96% using window width of 75-150 ms.
These results open new and promising possibilities for the development of invasive cortical neural prostheses for the control of reaching and grasping.
在最近的一段时间里，已经开发出了几种侵入性皮质神经假体。已经对从运动皮层（区域 MI）记录的信号进行了解码，并将其用于控制计算机光标和机器人设备。然而，很少有尝试来预测不同的抓握方式。使用来自两只猴子的运动神经元在伸手抓握任务期间从腹侧前运动皮层（区域 F5）记录的信号，支持向量机（SVM）分类器已经被训练用于对四种/六种抓握类型的连续解码。
结果表明，使用 75-150ms 的窗口宽度，可以以高于 96％的分类准确性提取四种/六种抓握类型。
这些结果为开发用于控制伸手和抓握的侵入性皮质神经假体开辟了新的、有前途的可能性。
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