Degenhart Alan D, Collinger Jennifer L, Vinjamuri Ramana, Kelly John W, Tyler-Kabara Elizabeth C, Wang Wei
University of Pittsburgh, Pittsburgh, PA, USA.
Annu Int Conf IEEE Eng Med Biol Soc. 2011;2011:5782-5. doi: 10.1109/IEMBS.2011.6091431.
In the presented work, standard and high-density electrocorticographic (ECoG) electrodes were used to record cortical field potentials in three human subjects during a hand posture task requiring the application of specific levels of force during grasping. We show two-class classification accuracies of up to 80% are obtained when classifying between two-finger pinch and whole-hand grasp hand postures despite differences in applied force levels across trials. Furthermore, we show that a four-class classification accuracy of 50% is achieved when predicting both hand posture and force level during a two-force, two-hand-posture grasping task, with hand posture most reliably predicted during high-force trials. These results suggest that the application of force plays a significant role in ECoG signal modulation observed during motor tasks, emphasizing the potential for electrocorticography to serve as a source of control signals for dexterous neuroprosthetic devices.
在本研究中，使用标准和高密度皮质脑电图（ECoG）电极，在一项手部姿势任务中记录三名人类受试者的皮质场电位，该任务要求在抓握过程中施加特定水平的力。我们发现，尽管各试验中施加的力水平存在差异，但在对两指捏和全手抓握两种手部姿势进行分类时，两类分类准确率高达80%。此外，我们还表明，在一项双力、双手姿势抓握任务中预测手部姿势和力水平时，四类分类准确率达到了50%，其中在高力试验中手部姿势的预测最为可靠。这些结果表明，力的施加在运动任务中观察到的ECoG信号调制中起着重要作用，强调了皮质脑电图作为灵巧神经假体装置控制信号源的潜力。
