Electromyography and ultrasound investigation of upper-arm muscles

我的研究项目是面向上肢肌肉激活的研究。重点是多功能的肱二头肌，因为生理上，它的运动单位在肌肉的不同区域被激活，这取决于手臂的位置，解剖上，因为多达6个单独受神经支配的隔间可以在其内表面上观察到。我们的目标是发现这些隔室是如何自愿收缩的，从而表现为肌肉中的“肌肉”。这一基本方面对上肢截肢者有实际影响，他们使用需要许多肌电图（EMG）控制信号的现代假肢，以充分受益于他们产生许多运动的能力。** 在我们的实验室中，EMG记录是用10个表面电极对完成的，这些电极对位于10名正常受试者的二头肌上，他们在二头肌收缩时实验了不同的上肢和手的位置。对于这些表面信号，将与二头肌组织内的电偶极子的存在相关联。这些偶极子的位置将通过应用于有限元上臂模型的逆问题求解方法来完成。该模型将使用上臂磁共振图像上获得的信息建立。为了在该模型中包括隔室的存在，将对从人二头肌获得的高分辨率磁图像进行分割，以识别隔室的位置和大小，所有隔室都沿着其二头肌的长度。** 当我们的手姿势改变时，皮肤下的组织会发生变形，可以用超声（US）设备进行成像。 在所获得的图像上，将使用半自动分割算法来识别二头肌的短头和长头、肱骨的顶部以及头静脉和贵要静脉。在这些识别的结构之间，将测量距离和角位置，并测量二头肌短头和长头的面积。这些皮肤下的变化将用于表面肌电信号的分析。** 在收缩的二头肌内，将使用US弹性成像识别高刚度应力区，在与EMG记录相似的条件下获得，预计高刚度区与电偶极子位置重叠。** 最近，从许多表面EMG信号中提取肌肉协同作用被证明对控制假肢是有用的。我们发现，肌肉协同作用也可以从一个单一的肌肉，如二头肌提取。作为概念的证明，二头肌的5个信号的肌肉协同作用将用于具有小型人形机器人以在线复制正常受试者产生的手臂运动。** 融合几种方式，探索肌肉隔间和肌肉协同作用可能会导致更容易控制肌电假肢。 **************

My research program is oriented toward the study of upper limb muscles activation. Focus is on the multifunctional biceps brachii because physiologically, its motor units are activated in different regions of the muscle depending on the arm position, and anatomically because of up to 6 individually innervated compartments can be observed on its inner surface. Our objective is to find how those compartments could voluntarily be contracted and thus behave as “muscles” within a muscle. This fundamental aspect has a practical impact for upper limb amputees who use modern prostheses requiring many electromyographic (EMG) control signals to fully benefit from their ability to produce many movements. ******In our lab, EMG recordings were done with 10 surface electrode pairs positioned across the biceps of 10 normal subjects who experimented different upper limb and hand positions while their biceps was contracted. To those surface signals, will be associated the presence of electric dipoles within the biceps tissue. Location of those dipoles will be done with an inverse problem solving method applied on a finite element upper arm model. This model will be established with information obtained on upper arm magnetic resonance images. To include the presence of compartments within this model, high resolution magnetic images obtained from human biceps will be segmented to identify location and size of the compartments all along the length of their biceps. ******As our hand posture changes, tissues under the skin are subjected to deformations which can be imaged with an ultrasound (US) equipment. On the obtained images a semi-automatic segmentation algorithm will be used to identify the short and long head of the biceps, the top of the humerus bone and the cephalic and basilic veins. Between those identified structures, distance and angular position will be measured and area of the short and long heads of the biceps measured. Those under the skin changes will be used in the analysis of surface EMG signals. ******Within a contracted biceps, zones of high stiffness stress will be identified with US elastography, Obtained in condition similar to those of EMG recording, high stiffness zones are expected to overlap the electric dipole positions. ******Recently, extraction of muscular synergies from many surface EMG signals proved to be useful to control a prosthesis. We found that muscle synergy could also be extracted from a single muscle such as the biceps. As a proof of concept, muscle synergies of 5 signals of the biceps will be used to have a small humanoid robot to on-line duplicate arm movements produced by normal subjects. ******Fusion of several modalities, exploration of muscle compartments and muscular synergy could lead to an easier control of myoelectric prostheses. **************