Multiple Feature Fusion for Automatic Emotion Recognition Using EEG Signals

Liu, N. and Fang, Y. and Li, L. and Hou, L. and Yang, F. and Guo, Y. (2018) Multiple Feature Fusion for Automatic Emotion Recognition Using EEG Signals. In: 2018 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), 15-20 April 2018, Calgary, AB, Canada. (doi:https://doi.org/10.1109/ICASSP.2018.8462518) (Access to this publication is currently restricted. You may be able to access a copy if URLs are provided)

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Official URL
http://dx.doi.org/10.1109/ICASSP.2018.8462518

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.

Item Type: Conference or workshop item (Paper)
Uncontrolled keywords: cepstral analysis;computer vision;electroencephalography;emotion recognition;feature extraction;gradient methods;image fusion;learning (artificial intelligence);medical signal processing;pattern classification;multiple feature fusion;automatic emotion recognition;vanishing gradient problem;exploding gradient problem;computer vision;profound semantic information;frequency features;signal processing area;raw EEG signals;emotion classification performance;electroencephalo graphic signals;deep residual networks;k nearst neighbor;pre trained ResNets;linear frequency cepstral coefficients;Electroencephalography;Task analysis;Emotion recognition;Brain modeling;Feature extraction;Databases;Speech recognition;emotion recognition;EEG;Residual Networks;cepstral coefficients
Divisions: Faculties > Sciences > School of Computing
Faculties > Sciences > School of Computing > Computational Intelligence Group
Faculties > Sciences > School of Computing > Data Science
Depositing User: Caroline Li
Date Deposited: 23 Oct 2018 19:04 UTC
Last Modified: 24 Oct 2018 11:13 UTC
Resource URI: https://kar.kent.ac.uk/id/eprint/69762 (The current URI for this page, for reference purposes)
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