Abstract: one kind of fusion wavelet characteristic and the generalized distinction analysis （GDA） extraction method was proposed and it was applied to descript the efficiency of dimension reduction for multi-scale feature of SEMG. First, wavelet decompose was employed on SEMG, and the mean of absolute value for wavelet coefficient at each level was extracted as original feature vector, then the dimension by GDA was reduced, the new feature of lower dimension was obtained, which was used for classification by Bayes classifier. By selecting felicitous levels of wavelet decomposition as well as pretty kernel parameter and new dimension, it can result in good performance in the pattern recognition of six movements including wrist inward，wrist outward, wrist up, wrist down, fist clench and fist stretch with the accuracy of above 97 % for three testers. The research indicated that the proposed method can successfully acquire the main component of multi-scale SEMG and its feature.

Key words: signal analysis, wavelet features, GDA, bayes classifier, pattern recognition

摘要： 提出一种融合小波特征和广义判别分析的特征解析方法，并对动作表面肌电信号的多尺度特征进行有效降维描述。首先，通过对表面肌电信号进行小波分解，提取各尺度上小波系数绝对值均值作为原始特征向量，然后用广义判别分析方法进行降维，得到低维的新特征向量，用贝叶斯分类器进行降维有效性检验。结果显示，对选用的三种小波，通过选取恰当的小波分解层数，核参数以及新特征向量的维数，对三名受试者前臂6种动作模式（内翻，外翻，握拳，展拳，上切和下切）的正确识别率可以达到97 %以上。研究表明，该方法能很好地获取表面肌电信号的多尺度主要成分及其特性。

关键词: 信号分析, 小波特征, 广义判别分析, 贝叶斯分类器, 模式识别