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首页> 外文期刊>WSEAS Transactions on Signal Processing >Human Identification based on electroencephalography Signals using Sample Entropy and Horizontal Visibility Graphs
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Human Identification based on electroencephalography Signals using Sample Entropy and Horizontal Visibility Graphs

机译：使用样品熵和水平可见性图，基于脑电图信号的人体识别

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摘要

Biometric development depends on electroencephalography (EEG) distinguishes people by utilizing individual qualities in human brainwaves. Tow Essential features of EEG signals are Liveliness strength against adulteration. However, far reaching study on human authentication utilizing EEG signals is still remain. In this paper we propose a two-phase approach to distinguish EEG signals. The first phase, feature vectors are based on Sample Entropy (SaE) and Horizontal Visibility Graphs (HVG) to extract feature vector of EEG activities. The second phase performs a classification of these feature vectors using K-Nearest Neighbour (KNN) classifiers. We test the accuracy of the proposed approach on Machine Learning Repository (UCI) dataset. Experimental results on this dataset demonstrate significant improvement in the classification accuracy compared to other reported results. Our study applied two models, the first model using 13 channels to feature extraction. It was found that classifier with HVG had a much better performance giving the highest accuracy gave 94.8% compared to classifier with SaE gave 83.7% accuracy. The second model using all channels. The classification accuracy with HVG gave 97.4% and with SaE gave 92.6%.
机译：生物识别发展取决于脑电图（EEG）通过利用人类脑波中的个别质量来区分人。脑电图信号的基本特征是掺杂掺杂的活性强度。然而，仍然存在对利用EEG信号的人类认证的远程研究。在本文中，我们提出了一种以两相方法来区分EEG信号。第一阶段，特征向量基于样本熵（SAE）和水平可见性图（HVG）以提取EEG活动的特征向量。第二阶段使用K-Collect邻居（KNN）分类器执行这些特征向量的分类。我们测试机器学习存储库（UCI）数据集的提出方法的准确性。与其他报告的结果相比，该数据集上的实验结果表明了分类准确性的显着改善。我们的研究应用了两种型号，使用13个通道的第一个模型来特征提取。结果发现，与具有SAE的分类器相比，具有HVG的分类器具有更好的性能，其比例为94.8％，精度为83.7％。使用所有通道的第二种模型。 HVG的分类准确性得到97.4％，SAE获得92.6％。

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