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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...
Autores principales: | , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Hindawi
2018
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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. |
format | Online Article Text |
id | pubmed-6057426 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2018 |
publisher | Hindawi |
record_format | MEDLINE/PubMed |
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 |
work_keys_str_mv | AT weiwei emotionrecognitionbasedonweightedfusionstrategyofmultichannelphysiologicalsignals AT jiaqingxuan emotionrecognitionbasedonweightedfusionstrategyofmultichannelphysiologicalsignals AT fengyongli emotionrecognitionbasedonweightedfusionstrategyofmultichannelphysiologicalsignals AT chengang emotionrecognitionbasedonweightedfusionstrategyofmultichannelphysiologicalsignals |