EEG Signal Classification with Deep Neural Networks using Visibility Graphs

Turan Goktug Altundogan, Mehmet Karaköse
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Abstract

EEG signals are data presented by collecting electrical activities in the brain at a certain frequency. Today, applications using the EEG signal are implemented in many fields such as medicine, computer science, robotic. Visibility Graphs, on the other hand, are graphs where certain points are associated according to their visibility features in order to perform mapping and operations in areas such as robotics. Visibility Graphs are also used today to express signals. In this study, the EEG signals are expressed with visibility graphs after certain pre-processing. Then, the classification of the obtained graph depending on the clique and degree features was carried out by using deep artificial neural networks. EEG signals have a very noisy nature, and complex pre-processing and feature extractions are used in applications using EEG signals. In the proposed method, EEG signals are subjected to very simple pre-processing and classified with a 95% success rate.
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基于可见性图的深度神经网络脑电信号分类
脑电图信号是通过收集大脑中一定频率的电活动而呈现的数据。今天,使用脑电图信号的应用在许多领域实现,如医学,计算机科学,机器人。另一方面,可见性图是根据其可见性特征将某些点关联起来的图,以便在机器人等领域执行映射和操作。可见性图今天也被用来表达信号。在本研究中,脑电信号经过一定的预处理后用可见性图表示。然后,利用深度人工神经网络对得到的图根据团和度特征进行分类;脑电信号具有很强的噪声特性,在使用脑电信号的应用中需要进行复杂的预处理和特征提取。该方法对脑电信号进行简单的预处理,分类成功率达95%。
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