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A Personal Classification Method Using Spatial Information of Multi-channel EEG

机译:利用多通道脑电图空间信息的个人分类方法

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Biometric authentication using various biological information is studied by many researchers. We study a feature extraction method available for personal authentication by focusing on EEG in the biological information. In addition, since electroencephalograph technology has advanced significantly in recent years, multichannel EEG is possible to be relatively easily measured Therefore, in this paper, as EEG features, we propose a method using a cross-correlation between electrodes obtained from the multi-channel electroencephalograph. In validations, a feature combination, which is obtained from the proposed method and time-frequency analysis, is used A personal classification is performed by applying SVM to obtained features. Moreover, by detailed validations about the proposed method, we evaluate the possibility of the cross-correlation between electrodes as features for the personal authentication.
机译:许多研究人员研究了使用各种生物信息的生物特征认证。通过研究生物信息中的脑电图,我们研究了一种可用于个人认证的特征提取方法。另外,由于近年来脑电图仪技术的显着发展,因此多通道脑电图的测量相对容易。因此,在本文中,由于脑电图的特点,我们提出了一种使用从多通道脑电图仪获得的电极之间互相关的方法。 。在验证中,使用从建议的方法和时频分析获得的特征组合。通过将SVM应用于获得的特征来执行个人分类。此外,通过对提出的方法的详细验证,我们评估了电极之间互相关作为个人身份验证功能的可能性。

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