Motor cortical control of movement speed with implications for brain-machine interface control
Motor cortical control of movement speed with implications for brain-machine interface control
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DOI：
10.1152/jn.00391.2013
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发表时间：
2014-07-01
影响因子：
2.5
通讯作者：
Chase, Steven M.
中科院分区：
文献类型：
--
作者：
Golub, Matthew D.;Yu, Byron M.;Chase, Steven M.
运动皮质在驱动运动方面起着重要作用，但这种控制的细节仍未解决。我们分析了从猴子进行中心外伸时记录的单次运动皮质活动中提取与运动相关的信息的程度。利用信息论技术，我们发现，与方向相关的信息相比，单个单元携带的速度相关信息相对较少。这一结果在人口层面上没有得到缓解：同时记录的人口活动预测速度的准确性明显低于方向预测。此外，一项单位下降分析显示，即使在人口较多的情况下，速度精度也可能低于方向精度。这些结果表明，使用通常假设的编码方案很难提取单次试验移动速度的瞬时细节。这种明显缺乏的速度信息在依赖于从运动皮质提取运动学信息的脑机接口(BMI)的背景下尤为重要。以前的研究已经强调了受试者在保持BMI光标稳定在目标位置上的困难。这些研究，加上我们在运动皮质中发现的相对较少的速度信息，启发了一种速度抑制卡尔曼过滤器(SDKF)，它在检测到解码的移动方向的变化时自动减慢光标的速度。有效地，SDKF通过使用流行的方向信号来增强速度控制，而不是要求速度直接从神经活动中解码。与标准卡尔曼滤波相比，SDKF将成功率提高了1.7倍，在需要在目标处稳定停止的闭环BMI任务中。当转化为临床应用时，支持稳定停止的BMI系统将更加有效和用户友好。
Motor cortex plays a substantial role in driving movement, yet the details underlying this control remain unresolved. We analyzed the extent to which movement-related information could be extracted from single-trial motor cortical activity recorded while monkeys performed center-out reaching. Using information theoretic techniques, we found that single units carry relatively little speed-related information compared with direction-related information. This result is not mitigated at the population level: simultaneously recorded population activity predicted speed with significantly lower accuracy relative to direction predictions. Furthermore, a unit-dropping analysis revealed that speed accuracy would likely remain lower than direction accuracy, even given larger populations. These results suggest that the instantaneous details of single-trial movement speed are difficult to extract using commonly assumed coding schemes. This apparent paucity of speed information takes particular importance in the context of brain-machine interfaces (BMIs), which rely on extracting kinematic information from motor cortex. Previous studies have highlighted subjects' difficulties in holding a BMI cursor stable at targets. These studies, along with our finding of relatively little speed information in motor cortex, inspired a speed-dampening Kalman filter (SDKF) that automatically slows the cursor upon detecting changes in decoded movement direction. Effectively, SDKF enhances speed control by using prevalent directional signals, rather than requiring speed to be directly decoded from neural activity. SDKF improved success rates by a factor of 1.7 relative to a standard Kalman filter in a closed-loop BMI task requiring stable stops at targets. BMI systems enabling stable stops will be more effective and user-friendly when translated into clinical applications.