About: Multiple Feature Fusion for Automatic Emotion Recognition Using EEG Signals

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type	http://eprints.org/ontology/ConferenceItemEPrint http://eprints.org/ontology/EPrint Article Academic Article
seeAlso	HTML Summary of #69762 Multiple Feature Fusion for Automatic Emotion Recognition Using EEG Signals
sameAs	Multiple Feature Fusion for Automatic Emotion Recognition Using EEG Signals
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http://eprints.org/ontology/hasDocument	Multiple Feature Fusion for Automatic Emotion Recognition Using EEG Signals (PDF) Multiple Feature Fusion for Automatic Emotion Recognition Using EEG Signals (Other) Multiple Feature Fusion for Automatic Emotion Recognition Using EEG Signals (Other) Multiple Feature Fusion for Automatic Emotion Recognition Using EEG Signals (Other) Multiple Feature Fusion for Automatic Emotion Recognition Using EEG Signals (Other) Multiple Feature Fusion for Automatic Emotion Recognition Using EEG Signals (Other) Multiple Feature Fusion for Automatic Emotion Recognition Using EEG Signals (PDF) Multiple Feature Fusion for Automatic Emotion Recognition Using EEG Signals (Other) Multiple Feature Fusion for Automatic Emotion Recognition Using EEG Signals (Other) Multiple Feature Fusion for Automatic Emotion Recognition Using EEG Signals (Other) Multiple Feature Fusion for Automatic Emotion Recognition Using EEG Signals (Other) Multiple Feature Fusion for Automatic Emotion Recognition Using EEG Signals (Other)
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dc:hasVersion	Multiple Feature Fusion for Automatic Emotion Recognition Using EEG Signals (PDF) Multiple Feature Fusion for Automatic Emotion Recognition Using EEG Signals (PDF)
Title	Multiple Feature Fusion for Automatic Emotion Recognition Using EEG Signals
described by	https://demo.openlinksw.com/about/id/entity/https/www.cs.kent.ac.uk/people/staff/akj22/materials/CO644/export_kar_RDFN3.n3
Date	2018-09-13
Creator	L. Hou Y. Fang N. Liu L. Li F. Yang Y. Guo
status	peer reviewed published
Publisher	IEEE
abstract	Automatic emotion recognition based on electroencephalo-graphic (EEG) signals has received increasing attention in recent years. The Deep Residual Networks (ResNets) can solve vanishing gradient problem and exploding gradient problem well in computer vision and can learn more profound semantic information. And for traditional methods, frequency features often play important role in signal processing area. Thus, in this paper, we use the pre-trained ResNets to extract deep semantic information and the linear-frequency cepstral coefficients (LFCC) as features from raw EEG signals. Then the two features are fused to improve the emotion classification performance of our approach. Moreover, several classifiers are used for our fused features to evaluate the performance and it shows that the proposed approach is effective for emotion classification. We find that the best performance is achieved when use k-nearst neighbor (KNN) as classifier, and we provide a detailed discussion for the reason.
Is Part Of	https://kar.kent.ac.uk/id/repository
list of authors	https://kar.kent.ac.uk/id/eprint/69762#authors
presented at	2018 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)
is topic of	https://www.cs.kent.ac.uk/people/staff/akj22/materials/CO644/export_kar_RDFN3.n3
is primary topic of	HTML Summary of #69762 Multiple Feature Fusion for Automatic Emotion Recognition Using EEG Signals

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