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Wavelet packet transform for feature extraction of EEG during mental tasks

机译：小波包变换在脑电任务脑电特征提取中的应用

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Wavelet packet transform (WPT) based feature extraction of the electroencephalogram (EEG) is introduced. Six-channel EEG data of four subjects were recorded while they performed three different mental tasks. Approximate one-second data segments were divided and transformed to multi-scale representations by dyadic wavelet packet decomposition channel by channel. Power values of different sub-spaces of six-channel EEG signals formed the feature vectors. A radial basis function (RBF) network was applied to classify the three task pairs. The average classification accuracy of four subjects over three task pairs is 85.3%. Compared with the two autoregressive (AR) model methods, wavelet packet transform would be a promising method to extract features from EEG signals.
机译：引入了基于小波包变换（WPT）脑电图（EEG）的特征提取。在执行三个不同的精神任务时记录四个受试者的六声道EEG数据。通过通道将近似的一秒数据段划分和转换为多达小波分组分解通道的多尺度表示。六通道EEG信号的不同子空间的功率值形成了特征向量。应用径向基函数（RBF）网络来对三个任务对进行分类。三个任务对的四个受试者的平均分类准确性为85.3％。与两种自回归（AR）模型方法相比，小波包变换将是从EEG信号中提取特征的有希望的方法。

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