In this study, an efficient method for extracting and selecting features of unrefined Electroencephalogram (EEG) signals according to the one‐dimensional local binary pattern (1D‐LBP) is presented. Considering that taking a correct decision on various issues particularly in the field of diagnosing diseases, such as epilepsy, is of paramount importance, a functional approach is designed to extract the optimal features of EEG signals. The proposed method is comprised of two main steps: First, extraction and selection of features is performed based on a novel improved 1D‐LBP model followed by data normalization through principal component analysis (PCA); as combining 1D‐LBP neighboring models and PCA (1D‐LBP^c2p) method. The second step includes classification using two of the best ensemble classification algorithms, that is, random forest and rotation forest. A comparative evaluation is performed between the proposed methods and 13 distinct reported approaches including uniform and non‐uniform 1D‐LBP. The results are demonstrating that the combining method presented in our approaches has superiority along with efficiency by providing higher accuracy compared to the other models and classifiers. The proposed method in this paper can be considered as a new method for feature extraction and selection of other kinds of EEG signals and data sets.

中文翻译：

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基于改进的1D-LBP特征提取的随机森林和轮作林集成方法对癫痫脑电信号进行分类

在这项研究中，提出了一种根据一维局部二进制模式（1D-LBP）提取和选择未精炼脑电图（EEG）信号特征的有效方法。考虑到对各种问题做出正确的决定，尤其是在诸如癫痫病等疾病的诊断中，这一点至关重要，因此设计一种功能性方法来提取EEG信号的最佳特征。所提出的方法包括两个主要步骤：首先，基于新颖的改进的1D-LBP模型执行特征的提取和选择，然后通过主成分分析（PCA）进行数据归一化；结合1D-LBP相邻模型和PCA（1D-LBP ^c2p） 方法。第二步包括使用两种最佳集成分类算法进行分类，即随机森林和旋转森林。在提议的方法和13种不同的报告方法之间进行了比较评估，包括统一和非统一的1D-LBP。结果表明，与其他模型和分类器相比，通过提供更高的准确性，我们的方法中提出的合并方法具有更高的效率。本文提出的方法可以被认为是一种特征提取和选择其他类型的脑电信号和数据集的新方法。