Chu Jun-Uk, Moon Inhyuk, Mun Mu-Seong
Korea Orthopedics and Rehabilitation Engineering Center, Incheon 403-712, Korea.
IEEE Trans Biomed Eng. 2006 Nov;53(11):2232-9. doi: 10.1109/TBME.2006.883695.
This paper proposes a novel real-time electromyogram (EMG) pattern recognition for the control of a multifunction myoelectric hand from four channel EMG signals. To extract a feature vector from the EMG signal, we use a wavelet packet transform that is a generalized version of wavelet transform. For dimensionality reduction and nonlinear mapping of the features, we also propose a linear-nonlinear feature projection composed of principal components analysis (PCA) and a self-organizing feature map (SOFM). The dimensionality reduction by PCA simplifies the structure of the classifier and reduces processing time for the pattern recognition. The nonlinear mapping by SOFM transforms the PCA-reduced features into a new feature space with high class separability. Finally, a multilayer perceptron (MLP) is used as the classifier. Using an analysis of class separability by feature projections, we show that the recognition accuracy depends more on the class separability of the projected features than on the MLP's class separation ability. Consequently, the proposed linear-nonlinear projection method improves class separability and recognition accuracy. We implement a real-time control system for a multifunction virtual hand. Our experimental results show that all processes, including virtual hand control, are completed within 125 ms, and the proposed method is applicable to real-time myoelectric hand control without an operational time delay.
本文提出了一种新颖的实时肌电图（EMG）模式识别方法，用于通过四通道EMG信号控制多功能肌电手。为了从EMG信号中提取特征向量，我们使用了小波包变换，它是小波变换的广义形式。为了对特征进行降维和非线性映射，我们还提出了一种由主成分分析（PCA）和自组织特征映射（SOFM）组成的线性-非线性特征投影。PCA进行的降维简化了分类器的结构，并减少了模式识别的处理时间。SOFM进行的非线性映射将PCA降维后的特征变换到具有高类可分性的新特征空间。最后，使用多层感知器（MLP）作为分类器。通过对特征投影的类可分性分析，我们表明识别准确率更多地取决于投影特征的类可分性，而不是MLP的类分离能力。因此，所提出的线性-非线性投影方法提高了类可分性和识别准确率。我们实现了一个用于多功能虚拟手的实时控制系统。我们的实验结果表明，包括虚拟手控制在内的所有过程都能在125毫秒内完成，并且所提出的方法适用于实时肌电手控制，不存在操作时间延迟。
