NDLI: Identifying valence and arousal levels via connectivity between EEG channels

Content Provider	IEEE Xplore Digital Library
Author	Mo Chen Junwei Han Lei Guo Jiahui Wang Patras, I.
Copyright Year	2015
Description	Author affiliation: Sch. of Electron. Eng. & Comput. Sci., Queen Mary Univ. of London, London, UK (Patras, I.) \|\| Sch. of Autom., Northwestern Polytech. Univ., Xi'an, China (Mo Chen; Junwei Han; Lei Guo; Jiahui Wang)
Abstract	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.
Starting Page	63
Ending Page	69
File Size	293532
Page Count	7
File Format	PDF
ISSN	21568111
e-ISBN	9781479999538
DOI	10.1109/ACII.2015.7344552
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2015-09-21
Publisher Place	China
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Electrodes Affective computing Correlation EEG Tagging Feature extraction Connectivity Electroencephalography Random variables Implicit emotion tagging Mutual information
Content Type	Text
Resource Type	Article

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