Grasp detection from human ECoG during natural reach-to-grasp movements.
Grasp detection from human ECoG during natural reach-to-grasp movements.
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DOI：
10.1371/journal.pone.0054658
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发表时间：
2013
期刊：
影响因子：
3.7
通讯作者：
Mehring C
中科院分区：
文献类型：
--
作者：
Pistohl T;Schmidt TS;Ball T;Schulze-Bonhage A;Aertsen A;Mehring C
抓取运动的各种运动参数，例如抓取的速度或类型，已从神经活动中成功解码。然而，从大脑活动检测运动事件的问题，即解码事件发生的时间（例如运动开始），却很少被解决。然而，这可能是一个非常重要的话题，因为控制抓取假体的脑机接口（BMI）可以通过检测抓取时间以及对要应用的抓取类型的可选解码来实现。因此，我们研究了在一系列自然且连续的抓握动作期间，从人类 ECoG 记录中检测抓握时间。使用从运动皮层记录的信号，基于正则化线性判别分析的检测器能够以高可靠性检索抓握时间点，并且几乎没有错误检测。使用时域和频域信号分量的组合实现了最佳性能。灵敏度（通过正确检测的数量来衡量）和特异性（通过错误检测的数量来表示）在很大程度上取决于对检测的时间精度以及事件检测与事件发生时间之间的延迟施加的限制。将事件发生后的神经数据纳入解码分析，稍微提高了准确性，但是，提前 125 毫秒检测到抓取事件时也可以获得合理的性能。总之，我们的结果为使用 ECoG 抓取运动检测来控制抓取假体提供了良好的基础。
Various movement parameters of grasping movements, like velocity or type of the grasp, have been successfully decoded from neural activity. However, the question of movement event detection from brain activity, that is, decoding the time at which an event occurred (e.g. movement onset), has been addressed less often. Yet, this may be a topic of key importance, as a brain-machine interface (BMI) that controls a grasping prosthesis could be realized by detecting the time of grasp, together with an optional decoding of which type of grasp to apply. We, therefore, studied the detection of time of grasps from human ECoG recordings during a sequence of natural and continuous reach-to-grasp movements. Using signals recorded from the motor cortex, a detector based on regularized linear discriminant analysis was able to retrieve the time-point of grasp with high reliability and only few false detections. Best performance was achieved using a combination of signal components from time and frequency domains. Sensitivity, measured by the amount of correct detections, and specificity, represented by the amount of false detections, depended strongly on the imposed restrictions on temporal precision of detection and on the delay between event detection and the time the event occurred. Including neural data from after the event into the decoding analysis, slightly increased accuracy, however, reasonable performance could also be obtained when grasping events were detected 125 ms in advance. In summary, our results provide a good basis for using detection of grasping movements from ECoG to control a grasping prosthesis.
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影响因子：
--
作者：
Bashashati A;Ward RK;Birch GE
通讯作者：
Birch GE
DOI：
10.1523/jneurosci.2081-09.2009
发表时间：
2009-11-11
期刊：
The Journal of neuroscience : the official journal of the Society for Neuroscience
影响因子：
--
作者：
Hwang EJ;Andersen RA
通讯作者：
Andersen RA
影响因子：
14.5
作者：
Yousry, TA;Schmid, UD;Winkler, P
通讯作者：
Winkler, P
影响因子：
3
作者：
Solis-Escalante, Teodoro;Mueller-Putz, Gernot;Pfurtscheller, Gert
通讯作者：
Pfurtscheller, Gert
影响因子：
64.8
作者：
Velliste, Meel;Perel, Sagi;Schwartz, Andrew B.
通讯作者：
Schwartz, Andrew B.