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OPTIMAL INPUT FEATURES SELECTION OF WAVELET-BASED EEG SIGNALS USING GA

机译：基于遗传算法的基于小波的脑电信号最优输入特征选择

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We present a method of selecting optimal input features from wavelet coefficients of electroencephalogram (EEG) signals. A combination of genetic algorithm (GA) and artificial neural network (ANN) are used to select the relevant features. In this investigation, classification accuracy and the fraction of a number of features rejected per total features is used as the fitness function to be optimized. The mental tasks of EEG signals from six channels are decomposed into five levels using discrete wavelet transform (DWT) produces 24 sub-bands with 96 input features. The features used to describe each sub-band are average energy, standard deviation, kurtosis and skewness of the distribution. This optimal input features are classified into five classes of mental tasks. Two types of selection algorithms are compared i.e. roulette wheel selection (RWS) and stochastic universal sampling (SUS). Results show that 11 to 12 input features with average classification accuracy rate of 81% to 82% with RWS is achieved compared to 16 input features of the same accuracy when SUS is adopted. It can be concluded that RWS performs better than SUS in this study.
机译：我们提出了一种从脑电图（EEG）信号的小波系数中选择最佳输入特征的方法。遗传算法（GA）和人工神经网络（ANN）的组合用于选择相关特征。在这项研究中，将分类精度和每个总特征拒绝的多个特征的分数用作要优化的适应度函数。使用离散小波变换（DWT）将来自六个通道的脑电信号的精神任务分解为五个级别，从而产生具有96个输入特征的24个子带。用于描述每个子带的特征是平均能量，标准偏差，峰度和分布偏度。最佳输入功能分为五类心理任务。比较了两种选择算法，即轮盘赌轮选择（RWS）和随机通用抽样（SUS）。结果表明，与采用SUS时具有相同精度的16个输入特征相比，使用RWS可以实现11到12个输入特征，平均分类准确率达到81％到82％。可以得出结论，在本研究中RWS的性能优于SUS。

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