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Onset Detection to Study Muscle Activity in Reaching and Grasping Movements in Rats

机译:发作检测以研究大鼠伸手和抓地运动中的肌肉活动

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EMG signals reflect the neuromuscular activation patterns related to the execution of a certain movement or task. In this work, we focus on reaching and grasping (R&G) movements in rats. Our objective is to develop an automatic algorithm to detect the onsets and offsets of muscle activity and use it to study muscle latencies in R&G maneuvers.We had a dataset of intramuscular EMG signals containing 51 R&G attempts from 2 different animals. Simultaneous video recordings were used for segmentation and comparison. We developed an automatic onset/offset detector based on the ratio of local maxima of Teager-Kaiser Energy (TKE). Then, we applied it to compute muscle latencies and other features related to the muscle activation pattern during R&G cycles. The automatic onsets that we found were consistent with visual inspection and video labels. Despite the variability between attempts and animals, the two rats shared a sequential pattern of muscle activations. Statistical tests confirmed the differences between the latencies of the studied muscles during R&G tasks.This work provides an automatic tool to detect EMG onsets and offsets and conducts a preliminary characterization of muscle activation during R&G movements in rats. This kind of approaches and data processing algorithms can facilitate the studies on upper limb motor control and motor impairment after spinal cord injury or stroke.
机译:EMG信号反映了与某种运动或任务的执行有关的神经肌肉激活模式。在这项工作中,我们专注于达到并抓住大鼠的R&G运动。我们的目标是开发一种自动算法,以检测肌肉活动的起伏和偏移,并用于研究R&G动作中的肌肉潜伏期。我们有一个肌内肌电信号的数据集,其中包含来自2种不同动物的51次R&G尝试。同时使用视频记录进行分割和比较。我们根据Teager-Kaiser能量(TKE)的局部最大值的比率开发了一种自动开始/偏移检测器。然后,我们将其用于计算R&G周期中的肌肉潜伏期以及与肌肉激活模式相关的其他特征。我们发现的自动发作与视觉检查和视频标签一致。尽管尝试和动物之间存在差异,但两只大鼠共享肌肉激活的顺序模式。统计测试证实了R&G任务期间研究的肌肉潜伏期之间的差异。这项工作提供了一种自动工具来检测EMG发作和偏移,并对大鼠R&G运动过程中的肌肉活化进行了初步表征。这种方法和数据处理算法可以促进对脊髓损伤或中风后上肢运动控制和运动障碍的研究。

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