運動疲勞過程中腦電信號特征提取仿真
發(fā)布時間：2018-07-20 21:06
【摘要】：對運動疲勞過程中腦電信號特征進行準確提取,可以為運動疲勞損傷的治療提供科學依據(jù)。對運動疲勞過程中不同層次腦電信號中的腦電波進行分解是進行腦電信號特征提取的基礎,而傳統(tǒng)方法利用可預測性的選取嵌入維數(shù)方法計算腦電信號序列的嵌入維數(shù),利用相空間重構對整體信號特征提取,但是不能對不同層次腦電波進行分解,導致腦電信號特征提取精度差。提出基于模糊熵的運動疲勞過程中腦電信號特征提取方法。上述方法先融合于小波變換理論,將運動疲勞過程中不同層次腦電信號中的腦電波進行合理分解,計算不同層次腦電節(jié)律頻帶中小波系數(shù)的能量均值與均值差,將能量均值與均值差作為特征向量,構建FISHER線性分類器對運動疲勞中的意識疲勞信號分類。仿真結果表明,所提方法可以有效地完成對運動疲勞過程中腦電信號特征提取。
[Abstract]:The accurate extraction of EEG signals during exercise fatigue can provide scientific basis for the treatment of sports fatigue injury. The decomposition of EEG signals at different levels during exercise fatigue is the basis of EEG feature extraction, while the embedded dimension of EEG sequences is calculated by the traditional method, which is based on predictive selection of embedding dimension. The phase space reconstruction is used to extract the feature of the whole signal, but the EEG signal can not be decomposed at different levels, which leads to the poor precision of the feature extraction of the EEG signal. A method for feature extraction of EEG signals during exercise fatigue based on fuzzy entropy is proposed. Firstly, the method is combined with wavelet transform theory to decompose EEG waves in different levels of EEG during exercise fatigue, and calculate the difference of energy mean and mean value of wavelet coefficients in different levels of EEG rhythm frequency band. Based on the difference between mean energy and mean value as eigenvector, a fish linear classifier is constructed to classify the conscious fatigue signals in motion fatigue. Simulation results show that the proposed method can effectively extract the feature of EEG during exercise fatigue.
【作者單位】： 鄭州大學西亞斯國際學院;
【基金】：基金項目:2015年河南省教育技術裝備和實踐教育研究(GZS084)
【分類號】：R87;TN911.7
本文編號：2134775
[Abstract]:The accurate extraction of EEG signals during exercise fatigue can provide scientific basis for the treatment of sports fatigue injury. The decomposition of EEG signals at different levels during exercise fatigue is the basis of EEG feature extraction, while the embedded dimension of EEG sequences is calculated by the traditional method, which is based on predictive selection of embedding dimension. The phase space reconstruction is used to extract the feature of the whole signal, but the EEG signal can not be decomposed at different levels, which leads to the poor precision of the feature extraction of the EEG signal. A method for feature extraction of EEG signals during exercise fatigue based on fuzzy entropy is proposed. Firstly, the method is combined with wavelet transform theory to decompose EEG waves in different levels of EEG during exercise fatigue, and calculate the difference of energy mean and mean value of wavelet coefficients in different levels of EEG rhythm frequency band. Based on the difference between mean energy and mean value as eigenvector, a fish linear classifier is constructed to classify the conscious fatigue signals in motion fatigue. Simulation results show that the proposed method can effectively extract the feature of EEG during exercise fatigue.
【作者單位】： 鄭州大學西亞斯國際學院;
【基金】：基金項目:2015年河南省教育技術裝備和實踐教育研究(GZS084)
【分類號】：R87;TN911.7
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1 黃力宇,程敬之;急性輕中度缺氧對腦電信號復雜度的影響[J];航天醫(yī)學與醫(yī)學工程;2000年04期
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