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首页> 外文期刊>WSEAS Transactions on Signal Processing >Combining spectral and fractal features for emotion recognition on Electroencephalographic signals
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Combining spectral and fractal features for emotion recognition on Electroencephalographic signals

机译：结合频谱和分形特征以对脑电信号进行情绪识别

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摘要

Recent studies have attempted to recognize emotions by extracting spectral and fractal features from electroencephalographic signals; however, up to now none of them have combined these two features to recognize emotions. This paper aims at providing a comparison between an accuracy rate of an approach that recognizes emotions by extracting both spectral and fractal features with that of those that extract only one of these features. To this end, we designed and implemented a procedure that recognizes positive and negative emotions by extracting spectral, fractal, or both features. Next, using this procedure, we built three different approaches to recognize positive and negative emotions; the first one extracted both spectral and fractal features, whereas the other two extracted each type of feature separately. Then, the accuracy rate of the approaches was calculated and compared among them. The comparison showed that the spectral-fractal approach recognizes emotions more accurately than the spectral and fractal approaches in 96% and 79% of the time, respectively. This suggests that it is possible to develop a more effective emotion recognition method by extracting both spectral and fractal features than extracting only one type of them.
机译：最近的研究试图通过从脑电图信号中提取频谱和分形特征来识别情绪。但是，到目前为止，他们还没有一个结合这两个功能来识别情绪。本文旨在提供一种通过提取频谱特征和分形特征来识别情绪的方法的准确率与仅提取这些特征之一的方法的准确率之间的比较。为此，我们设计并实施了一种程序，该程序通过提取频谱，分形或两个特征来识别正面和负面情绪。接下来，使用此过程，我们构建了三种不同的方法来识别积极和消极的情绪。第一个提取光谱特征和分形特征，而其他两个分别提取每种类型的特征。然后，计算出方法的准确率并在其中进行比较。比较显示，分形方法比分形方法和分形方法分别在96％和79％的时间内更准确地识别情绪。这表明，与仅提取一种类型的特征相比，通过提取频谱特征和分形特征，可以开发出更有效的情绪识别方法。

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