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Identifying valence and arousal levels via connectivity between EEG channels

机译：通过EEG频道之间的连接识别价和唤醒水平

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摘要

Implicit emotion tagging is a central theme in the area of affective computing. To this end, Several physiological signals acquired from subjects can be employed, for example, electroencephalography (EEG) and functional magnetic resonance imaging (fMRI) from brain, electrocardiography (ECG) from cardiac activities, and other peripheral physiological signals, such as galvanic skin resistance, electromyogram (EMG), blood volume pressure etc. Brain is regarded as the place where emotional activities evoke. Determining affective states by observing brain activities directly is of therefore great interest. There are several published works that use EEG signals to identify affective states in different aspects with various stimuli, e.s., images, musics and videos. In this paper, we propose to adopt EEG connectivity between electrodes to identify subjects' affective levels in both valence and arousal space during video stimuli presentation. Three catagories of connectivity are adopted in magnitude and phase domains. One open accessed affective database, DEAP, is used as benchmark. We will show that with the proposed connectivity-based representation, the accuracy of affective levels identification tasks are higher than the same tasks in existing works based on same database.
机译：隐式情感标记是情感计算领域的中心主题。为此，可以使用从受试者获取的若干生理信号，例如脑电图（EEG）和来自心脏活动的心电图（ECG）的脑电图（EEG）和功能磁共振成像（FMRI），以及其他外周生理信号，例如电镀皮肤抗性，电灰度（EMG），血容量压力等脑被认为是情绪活动唤起的地方。因此，通过直接观察大脑活动来确定情感状态是极大的兴趣。有几种公布的作品，使用EEG信号在不同方面识别具有各种刺激，例如图像，音乐和视频的不同方面的情感状态。在本文中，我们建议采用电极之间的EEG连接，在视频刺激呈现期间识别价值和唤醒空间中的受试者的情感水平。在幅度和相位域中采用了三个连接的连接。一个开放访问的情感数据库，DEAP用作基准。我们将显示，通过提出的基于连接的表示，情感级别识别任务的准确性高于基于同一数据库的现有工作中的相同任务。

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