Gowda Suraj, Orsborn Amy L, Overduin Simon A, Moorman Helene G, Carmena Jose M
IEEE Trans Neural Syst Rehabil Eng. 2014 Sep;22(5):911-20. doi: 10.1109/TNSRE.2014.2309673. Epub 2014 Mar 5.
Brain-machine interfaces (BMIs) are dynamical systems whose properties ultimately influence performance. For instance, a 2-D BMI in which cursor position is controlled using a Kalman filter will, by default, create an attractor point that "pulls" the cursor to particular points in the workspace. If created unintentionally, such effects may not be beneficial for BMI performance. However, there have been few empirical studies exploring how various dynamical effects of closed-loop BMIs ultimately influence performance. In this work, we utilize experimental data from two macaque monkeys operating a closed-loop BMI to reach to 2-D targets and show that certain dynamical properties correlate with performance loss. We also show that other dynamical properties represent tradeoffs between naturally competing objectives, such as speed versus accuracy. These findings highlight the importance of fine-tuning the dynamical properties of closed-loop BMIs to optimize task-specific performance.
脑机接口（BMI）是动态系统，其属性最终会影响性能。例如，一个使用卡尔曼滤波器控制光标位置的二维BMI，默认情况下会创建一个吸引点，将光标“拉”到工作空间中的特定点。如果是无意中产生的，这种影响可能对BMI性能没有益处。然而，很少有实证研究探讨闭环BMI的各种动态效应最终如何影响性能。在这项工作中，我们利用来自两只操作闭环BMI以到达二维目标的猕猴的实验数据，表明某些动态属性与性能损失相关。我们还表明，其他动态属性代表了自然竞争目标之间的权衡，例如速度与准确性。这些发现凸显了微调闭环BMI的动态属性以优化特定任务性能的重要性。
