Matlack Charlie, Haddock Andrew, Moritz Chet T, Chizeck Howard J
Annu Int Conf IEEE Eng Med Biol Soc. 2014;2014:2553-6. doi: 10.1109/EMBC.2014.6944143.
Recent advances in intracortical brain-machine interfaces (BMIs) for position control have leveraged state estimators to decode intended movements from cortical activity. We revisit the underlying assumptions behind the use of Kalman filters in this context, focusing on the fact that identified cortical coding models capture closed-loop task dynamics. We show that closed-loop models can be partitioned, exposing feedback policies of the brain which are separate from interface and task dynamics. Changing task dynamics may cause the brain to change its control policy, and consequently the closed-loop dynamics. This may degrade performance of decoders upon switching from manual tasks to velocity-controlled BMI-mediated tasks. We provide experimental results showing that for the same manual cursor task, changing system order affects neural coding of movement. In one experimental condition force determines position directly, and in the other force determines cursor velocity. From this we draw an analogy to subjects transitioning from manual reaching tasks to velocity-controlled BMI tasks. We conclude with suggested principles for improving BMI decoder performance, including matching the controlled system order between manual and brain control, and identifying the brain's controller dynamics rather than complete closed-loop dynamics.
用于位置控制的皮质内脑机接口（BMI）的最新进展利用状态估计器从皮质活动中解码预期动作。我们重新审视了在此背景下使用卡尔曼滤波器背后的潜在假设，重点关注已识别的皮质编码模型捕获闭环任务动态这一事实。我们表明，闭环模型可以被划分，从而揭示大脑的反馈策略，这些策略与接口和任务动态是分开的。任务动态的变化可能会导致大脑改变其控制策略，进而改变闭环动态。从手动任务切换到速度控制的BMI介导任务时，这可能会降低解码器的性能。我们提供的实验结果表明，对于相同的手动光标任务，改变系统阶数会影响运动的神经编码。在一种实验条件下，力直接决定位置，而在另一种条件下，力决定光标速度。由此我们类比了从手动够物任务过渡到速度控制的BMI任务的受试者。我们最后提出了一些提高BMI解码器性能的建议原则，包括使手动控制和大脑控制之间的受控系统阶数相匹配，以及识别大脑的控制器动态而非完整的闭环动态。
