Electroencephalogram (EEG) signal classification is used in many applications. Typically, this classification is implemented based on methods which consist of two steps. These steps are known as the step of signal preprocessing and the step of the classification. The signal preprocessing step transforms initial signal into classification attributes. According to several studies, this transformation can result in the loss of some useful information and, consequently, the formed classification attributes are uncertain. This information loss can be taken into account if the classification attributes are fuzzy and the fuzzy classifiers are used at the step of classification itself. The transformation of initial EEG signal into fuzzy attributes needs one more procedure at the step of signal preprocessing. This procedure is fuzzification. An approach based on fuzzy classifiers for EEG signal classification is considered in this article. The approach is evaluated based on two classifiers: fuzzy decision tree and fuzzy random Forest. The classification accuracy is 99.5% for fuzzy decision tree and 99.3% for fuzzy random forest. The comparison with similar studies based on non-fuzzy classifiers indicates that fuzzy classifiers are effective tool for EEG signal classification and have best classification accuracy.

中文翻译：

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基于模糊分类器的脑电信号分类


脑电图 (EEG) 信号分类有许多应用。通常，这种分类是基于由两个步骤组成的方法来实现的。这些步骤称为信号预处理步骤和分类步骤。信号预处理步骤将初始信号转换为分类属性。根据多项研究，这种转换可能会导致一些有用信息的丢失，因此形成的分类属性是不确定的。如果分类属性是模糊的并且在分类本身的步骤中使用模糊分类器，则可以考虑这种信息损失。将初始脑电信号转化为模糊属性在信号预处理步骤中还需要多一道程序。这个过程就是模糊化。本文考虑了一种基于模糊分类器的脑电信号分类方法。该方法基于两个分类器进行评估：模糊决策树和模糊随机森林。模糊决策树的分类准确率为99.5%，模糊随机森林的分类准确率为99.3%。与基于非模糊分类器的类似研究的比较表明，模糊分类器是脑电信号分类的有效工具，并且具有最好的分类精度。