Khushaba Rami N
IEEE Trans Neural Syst Rehabil Eng. 2014 Jul;22(4):745-55. doi: 10.1109/TNSRE.2014.2304470. Epub 2014 Feb 11.
An inability to adapt myoelectric interfaces to a novel user's unique style of hand motion, or even to adapt to the motion style of an opposite limb upon which the interface is trained, are important factors inhibiting the practical application of myoelectric interfaces. This is mainly attributed to the individual differences in the exhibited electromyogram (EMG) signals generated by the muscles of different limbs. We propose in this paper a multiuser myoelectric interface which easily adapts to novel users and maintains good movement recognition performance. The main contribution is a framework for implementing style-independent feature transformation by using canonical correlation analysis (CCA) in which different users' data is projected onto a unified-style space. The proposed idea is summarized into three steps: 1) train a myoelectric pattern classifier on the set of style-independent features extracted from multiple users using the proposed CCA-based mapping; 2) create a new set of features describing the movements of a novel user during a quick calibration session; and 3) project the novel user's features onto a lower dimensional unified-style space with features maximally correlated with training data and classify accordingly. The proposed method has been validated on a set of eight intact-limbed subjects, left-and-right handed, performing ten classes of bilateral synchronous fingers movements with four electrodes on each forearm. The method was able to overcome individual differences through the style-independent framework with accuracies of > 83% across multiple users. Testing was also performed on a set of ten intact-limbed and six below-elbow amputee subjects as they performed finger and thumb movements. The proposed framework allowed us to train the classifier on a normal subject's data while subsequently testing it on an amputee's data after calibration with a performance of > 82% on average across all amputees.
肌电接口无法适应新用户独特的手部运动方式，甚至无法适应其训练时所使用的对侧肢体的运动方式，这些都是阻碍肌电接口实际应用的重要因素。这主要归因于不同肢体肌肉产生的肌电图（EMG）信号存在个体差异。本文提出了一种多用户肌电接口，它能够轻松适应新用户，并保持良好的运动识别性能。主要贡献在于构建了一个框架，通过使用典型相关分析（CCA）实现与风格无关的特征转换，即将不同用户的数据投影到统一风格空间。所提出的思路可概括为三个步骤：1）使用基于CCA的映射从多个用户提取的与风格无关的特征集上训练肌电模式分类器；2）在快速校准过程中创建一组描述新用户运动的新特征；3）将新用户的特征投影到与训练数据最大相关的低维统一风格空间，并据此进行分类。该方法已在一组八名肢体健全的受试者（包括左利手和右利手）上得到验证，他们使用每个前臂上的四个电极进行十类双侧同步手指运动。该方法能够通过与风格无关的框架克服个体差异，在多个用户中的准确率超过83%。还对一组十名肢体健全的受试者和六名肘下截肢受试者进行了测试，他们进行手指和拇指运动。所提出的框架使我们能够在正常受试者的数据上训练分类器，然后在校准后在截肢者的数据上进行测试，所有截肢者的平均性能超过82%。
