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首页> 外文会议>International Conference on Quality in Research >Relative wavelet bispectrum feature for alcoholic EEG signal classification using artificial neural network
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Relative wavelet bispectrum feature for alcoholic EEG signal classification using artificial neural network

机译：使用人工神经网络的含酒精EEG信号分类的相对小波BISPectrum特征

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This paper proposes a novel relative wavelet bispectrum (RWB) approach for EEG signal feature extraction method to differentiate the signal between the alcoholic over the non-alcoholic subjects. Firstly, the EEG signal is calculated for its autocorrelation frequencies as the basic step in the bispectrum calculation. Then, the discrete wavelet transform (DWT) is applied substituting the FFT which usually is used in the bispectrum calculation. Lastly, the relative value of each frequency band is calculated for both the approximation and the details parts, producing the RWB. The proposed methodology is implemented in an alcoholic automated detection system using 1200 data samples from UCI EEG Database for alcoholism. Based on the experiments, the setting value of lag in the autocorrelation calculation was evidently very influential on the recognition rate obtained, i.e. the maximum value for the lag was the best. Using cross validation, the highest results from RWB feature extraction method with ANN classifier achieved about 90% recognition rate.
机译：本文提出了一种用于EEG信号特征提取方法的新型相对小波BISPectrum（RWB）方法，以区分含酒会在非酒精受试者之间的信号。首先，将EEG信号用于其自相关频率作为BISPectrum计算中的基本步骤。然后，将离散小波变换（DWT）替换为替换通常用于BISPectrum计算的FFT。最后，为近似和细节部分计算每个频带的相对值，产生RWB。所提出的方法在酗酒的自动检测系统中实现了来自UCI EEG数据库的1200个数据样本的酗酒。基于实验，自相关计算中滞后的设定值显着非常有影响力，即获得的识别率，即滞后的最大值是最好的。使用交叉验证，来自RWB功能提取方法的最高结果，ANN分类器实现了大约90 ％识别率。

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