Suminski Aaron J, Willett Francis R, Fagg Andrew H, Bodenhamer Matthew, Hatsopoulos Nicholas G
Department of Organismal Biology and Anatomy at the University of Chicago, Chicago, IL 60637, USA.
Annu Int Conf IEEE Eng Med Biol Soc. 2011;2011:5802-6. doi: 10.1109/IEMBS.2011.6091436.
Although most brain-machine interface (BMI) studies have focused on decoding kinematic parameters of motion, it is known that motor cortical activity also correlates with kinetic signals, including hand force and joint torque. In this experiment, a monkey used a cortically-controlled BMI to move a visual cursor and hit a sequence of randomly placed targets. By varying the contributions of separate kinetic and kinematic decoders to the movement of a virtual arm, we evaluated the hypothesis that a BMI incorporating both signals (Hybrid BMI) would outperform a BMI decoding kinematic information alone (Position BMI). We show that the trajectories generated by the Hybrid BMI during real-time decoding were straighter and smoother than those of the Position BMI. These results may have important implications for BMI applications that require controlling devices with inherent, physical dynamics or applying forces to the environment.
尽管大多数脑机接口（BMI）研究都集中在解码运动的运动学参数上，但众所周知，运动皮层活动也与动力学信号相关，包括手部力量和关节扭矩。在本实验中，一只猴子使用皮层控制的BMI来移动视觉光标并击中一系列随机放置的目标。通过改变单独的动力学和运动学解码器对虚拟手臂运动的贡献，我们评估了这样一个假设，即结合了两种信号的BMI（混合BMI）将优于仅解码运动学信息的BMI（位置BMI）。我们表明，在实时解码过程中，混合BMI生成的轨迹比位置BMI生成的轨迹更直、更平滑。这些结果可能对需要控制具有固有物理动力学的设备或向环境施加力的BMI应用具有重要意义。
