Machine Learning Laboratory, Berlin Institute of Technology, Berlin, Germany.
IEEE Trans Biomed Eng. 2012 May;59(5):1436-43. doi: 10.1109/TBME.2012.2188799. Epub 2012 Feb 23.
Pattern recognition techniques have been applied to extract information from electromyographic (EMG) signals that can be used to control electrical powered hand prostheses. In this paper, optimized spatial filters that enhance separation properties of EMG signals are investigated. In particular, different multiclass extensions of the common spatial patterns algorithm are applied to high-density surface EMG signals acquired from the forearms of ten healthy subjects. Visualization of the obtained filter coefficients provides insight into the physiology of the muscles related to the performed contractions. The CSP methods are compared with a commonly used pattern recognition approach in a six-class classification task. Cross-validation results show a significant improvement in performance and a higher robustness against noise than commonly used pattern recognition methods.
模式识别技术已被应用于从肌电图 (EMG) 信号中提取信息，这些信息可用于控制电动手假肢。本文研究了优化的空间滤波器，以增强 EMG 信号的分离特性。特别是，将通用空间模式算法的不同多类扩展应用于从十个健康受试者的前臂采集的高密度表面 EMG 信号。对获得的滤波器系数进行可视化处理，可以深入了解与所进行的收缩相关的肌肉的生理学。在六类分类任务中，将 CSP 方法与常用的模式识别方法进行了比较。交叉验证结果表明，与常用的模式识别方法相比，该方法在性能上有显著提高，对噪声的鲁棒性也更高。
