An improved feature extraction method is proposed aiming at the recognition of motor imagined electroencephalogram (EEG) signals. Using local mean decomposition, the algorithm decomposes the original signal into a series of product function (PF) components, and meaningless PF components are removed from EEG signals in the range of mu rhythm and beta rhythm. According to the principle of feature time selection, 4[Formula: see text]s to 6[Formula: see text]s motor imagery EEG signals are selected as classification data, and the sum of fuzzy entropies of second-and third-order PF components of [Formula: see text], [Formula: see text] lead signals is calculated, respectively. Mean value of fuzzy entropy [Formula: see text] is used as input element to construct EEG feature vector, and support vector machine (SVM) is used to classify and predict EEG signals for recognition. The test results show that this feature extraction method has higher classification accuracy than the empirical mode decomposition method and the total empirical mode decomposition method.

中文翻译：

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基于局部均值分解和模糊熵的脑电信号特征提取

针对运动想象脑电（EEG）信号的识别，提出了一种改进的特征提取方法。该算法采用局部均值分解，将原始信号分解为一系列乘积函数（PF）分量，并从脑电信号中去除mu节律和β节律范围内的无意义PF分量。根据特征时间选择原则，选取4[公式：见文]到6[公式：见文]s运动想象脑电信号作为分类数据，二阶和三阶PF的模糊熵之和分别计算[公式：见正文]、[公式：见正文]导联信号的分量。以模糊熵的平均值[公式：见正文]作为输入元素，构建EEG特征向量，支持向量机（SVM）用于对脑电信号进行分类和预测以进行识别。测试结果表明，该特征提取方法比经验模态分解法和全经验模态分解法具有更高的分类精度。