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Manifold Feature Fusion with Dynamical Feature Selection for Cross-Subject Emotion Recognition

Affective computing systems can decode cortical activities to facilitate emotional human–computer interaction. However, personalities exist in neurophysiological responses among different users of the brain–computer interface leads to a difficulty for designing a generic emotion recognizer that is a...

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Autores principales: Hua, Yue, Zhong, Xiaolong, Zhang, Bingxue, Yin, Zhong, Zhang, Jianhua
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8615971/
https://www.ncbi.nlm.nih.gov/pubmed/34827391
http://dx.doi.org/10.3390/brainsci11111392
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author Hua, Yue
Zhong, Xiaolong
Zhang, Bingxue
Yin, Zhong
Zhang, Jianhua
author_facet Hua, Yue
Zhong, Xiaolong
Zhang, Bingxue
Yin, Zhong
Zhang, Jianhua
author_sort Hua, Yue
collection PubMed
description Affective computing systems can decode cortical activities to facilitate emotional human–computer interaction. However, personalities exist in neurophysiological responses among different users of the brain–computer interface leads to a difficulty for designing a generic emotion recognizer that is adaptable to a novel individual. It thus brings an obstacle to achieve cross-subject emotion recognition (ER). To tackle this issue, in this study we propose a novel feature selection method, manifold feature fusion and dynamical feature selection (MF-DFS), under transfer learning principle to determine generalizable features that are stably sensitive to emotional variations. The MF-DFS framework takes the advantages of local geometrical information feature selection, domain adaptation based manifold learning, and dynamical feature selection to enhance the accuracy of the ER system. Based on three public databases, DEAP, MAHNOB-HCI and SEED, the performance of the MF-DFS is validated according to the leave-one-subject-out paradigm under two types of electroencephalography features. By defining three emotional classes of each affective dimension, the accuracy of the MF-DFS-based ER classifier is achieved at 0.50–0.48 (DEAP) and 0.46–0.50 (MAHNOBHCI) for arousal and valence emotional dimensions, respectively. For the SEED database, it achieves 0.40 for the valence dimension. The corresponding accuracy is significantly superior to several classical feature selection methods on multiple machine learning models.
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spelling pubmed-86159712021-11-26 Manifold Feature Fusion with Dynamical Feature Selection for Cross-Subject Emotion Recognition Hua, Yue Zhong, Xiaolong Zhang, Bingxue Yin, Zhong Zhang, Jianhua Brain Sci Article Affective computing systems can decode cortical activities to facilitate emotional human–computer interaction. However, personalities exist in neurophysiological responses among different users of the brain–computer interface leads to a difficulty for designing a generic emotion recognizer that is adaptable to a novel individual. It thus brings an obstacle to achieve cross-subject emotion recognition (ER). To tackle this issue, in this study we propose a novel feature selection method, manifold feature fusion and dynamical feature selection (MF-DFS), under transfer learning principle to determine generalizable features that are stably sensitive to emotional variations. The MF-DFS framework takes the advantages of local geometrical information feature selection, domain adaptation based manifold learning, and dynamical feature selection to enhance the accuracy of the ER system. Based on three public databases, DEAP, MAHNOB-HCI and SEED, the performance of the MF-DFS is validated according to the leave-one-subject-out paradigm under two types of electroencephalography features. By defining three emotional classes of each affective dimension, the accuracy of the MF-DFS-based ER classifier is achieved at 0.50–0.48 (DEAP) and 0.46–0.50 (MAHNOBHCI) for arousal and valence emotional dimensions, respectively. For the SEED database, it achieves 0.40 for the valence dimension. The corresponding accuracy is significantly superior to several classical feature selection methods on multiple machine learning models. MDPI 2021-10-23 /pmc/articles/PMC8615971/ /pubmed/34827391 http://dx.doi.org/10.3390/brainsci11111392 Text en © 2021 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).
spellingShingle Article
Hua, Yue
Zhong, Xiaolong
Zhang, Bingxue
Yin, Zhong
Zhang, Jianhua
Manifold Feature Fusion with Dynamical Feature Selection for Cross-Subject Emotion Recognition
title Manifold Feature Fusion with Dynamical Feature Selection for Cross-Subject Emotion Recognition
title_full Manifold Feature Fusion with Dynamical Feature Selection for Cross-Subject Emotion Recognition
title_fullStr Manifold Feature Fusion with Dynamical Feature Selection for Cross-Subject Emotion Recognition
title_full_unstemmed Manifold Feature Fusion with Dynamical Feature Selection for Cross-Subject Emotion Recognition
title_short Manifold Feature Fusion with Dynamical Feature Selection for Cross-Subject Emotion Recognition
title_sort manifold feature fusion with dynamical feature selection for cross-subject emotion recognition
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8615971/
https://www.ncbi.nlm.nih.gov/pubmed/34827391
http://dx.doi.org/10.3390/brainsci11111392
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AT yinzhong manifoldfeaturefusionwithdynamicalfeatureselectionforcrosssubjectemotionrecognition
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