模式识别(心理学)
人工智能
特征提取
支持向量机
脑电图
希尔伯特-黄变换
熵(时间箭头)
数学
模糊逻辑
近似熵
特征向量
计算机科学
语音识别
统计
能量(信号处理)
物理
心理学
量子力学
精神科
作者
Yanping Li,Qi Wang,Tao Wang,Jian Pei,Shuo Zhang
标识
DOI:10.1142/s0218001420580173
摘要
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.
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