Brain computer interface (BCI) systems have been regarded as a new way of communication for humans. In this research, common methods such as wavelet transform are applied in order to extract features. However, genetic algorithm (GA), as an evolutionary method, is used to select features. Finally, classification was done using the two approaches support vector machine (SVM) and Bayesian method. Five features were selected and the accuracy of Bayesian classification was measured to be 80% with dimension reduction. Ultimately, the classification accuracy reached 90.4% using SVM classifier. The results of the study indicate a better feature selection and the effective dimension reduction of these features, as well as a higher percentage of classification accuracy in comparison with other studies.

脑机接口（BCI）系统被认为是一种新的人类交流方式。在这项研究中，应用小波变换等常用方法来提取特征。然而，遗传算法（GA）作为一种进化方法，用于选择特征。最后，使用两种方法支持向量机（SVM）和贝叶斯方法进行分类。选择了五个特征，贝叶斯分类的准确率在降维后测量为 80%。最终，使用 SVM 分类器的分类准确率达到了 90.4%。研究结果表明，与其他研究相比，这些特征具有更好的特征选择和有效降维，以及更高的分类准确率。