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Emotion Recognition Based on Weighted Fusion Strategy of Multichannel Physiological Signals

Emotion recognition is an important pattern recognition problem that has inspired researchers for several areas. Various data from humans for emotion recognition have been developed, including visual, audio, and physiological signals data. This paper proposes a decision-level weight fusion strategy...

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Detalles Bibliográficos
Autores principales: Wei, Wei, Jia, Qingxuan, Feng, Yongli, Chen, Gang
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Hindawi 2018
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6057426/
https://www.ncbi.nlm.nih.gov/pubmed/30073024
http://dx.doi.org/10.1155/2018/5296523
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author Wei, Wei
Jia, Qingxuan
Feng, Yongli
Chen, Gang
author_facet Wei, Wei
Jia, Qingxuan
Feng, Yongli
Chen, Gang
author_sort Wei, Wei
collection PubMed
description Emotion recognition is an important pattern recognition problem that has inspired researchers for several areas. Various data from humans for emotion recognition have been developed, including visual, audio, and physiological signals data. This paper proposes a decision-level weight fusion strategy for emotion recognition in multichannel physiological signals. Firstly, we selected four kinds of physiological signals, including Electroencephalography (EEG), Electrocardiogram (ECG), Respiration Amplitude (RA), and Galvanic Skin Response (GSR). And various analysis domains have been used in physiological emotion features extraction. Secondly, we adopt feedback strategy for weight definition, according to recognition rate of each emotion of each physiological signal based on Support Vector Machine (SVM) classifier independently. Finally, we introduce weight in decision level by linear fusing weight matrix with classification result of each SVM classifier. The experiments on the MAHNOB-HCI database show the highest accuracy. The results also provide evidence and suggest a way for further developing a more specialized emotion recognition system based on multichannel data using weight fusion strategy.
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spelling pubmed-60574262018-08-02 Emotion Recognition Based on Weighted Fusion Strategy of Multichannel Physiological Signals Wei, Wei Jia, Qingxuan Feng, Yongli Chen, Gang Comput Intell Neurosci Research Article Emotion recognition is an important pattern recognition problem that has inspired researchers for several areas. Various data from humans for emotion recognition have been developed, including visual, audio, and physiological signals data. This paper proposes a decision-level weight fusion strategy for emotion recognition in multichannel physiological signals. Firstly, we selected four kinds of physiological signals, including Electroencephalography (EEG), Electrocardiogram (ECG), Respiration Amplitude (RA), and Galvanic Skin Response (GSR). And various analysis domains have been used in physiological emotion features extraction. Secondly, we adopt feedback strategy for weight definition, according to recognition rate of each emotion of each physiological signal based on Support Vector Machine (SVM) classifier independently. Finally, we introduce weight in decision level by linear fusing weight matrix with classification result of each SVM classifier. The experiments on the MAHNOB-HCI database show the highest accuracy. The results also provide evidence and suggest a way for further developing a more specialized emotion recognition system based on multichannel data using weight fusion strategy. Hindawi 2018-07-05 /pmc/articles/PMC6057426/ /pubmed/30073024 http://dx.doi.org/10.1155/2018/5296523 Text en Copyright © 2018 Wei Wei et al. https://creativecommons.org/licenses/by/4.0/ This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
spellingShingle Research Article
Wei, Wei
Jia, Qingxuan
Feng, Yongli
Chen, Gang
Emotion Recognition Based on Weighted Fusion Strategy of Multichannel Physiological Signals
title Emotion Recognition Based on Weighted Fusion Strategy of Multichannel Physiological Signals
title_full Emotion Recognition Based on Weighted Fusion Strategy of Multichannel Physiological Signals
title_fullStr Emotion Recognition Based on Weighted Fusion Strategy of Multichannel Physiological Signals
title_full_unstemmed Emotion Recognition Based on Weighted Fusion Strategy of Multichannel Physiological Signals
title_short Emotion Recognition Based on Weighted Fusion Strategy of Multichannel Physiological Signals
title_sort emotion recognition based on weighted fusion strategy of multichannel physiological signals
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6057426/
https://www.ncbi.nlm.nih.gov/pubmed/30073024
http://dx.doi.org/10.1155/2018/5296523
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AT fengyongli emotionrecognitionbasedonweightedfusionstrategyofmultichannelphysiologicalsignals
AT chengang emotionrecognitionbasedonweightedfusionstrategyofmultichannelphysiologicalsignals