Cargando…
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...
Autores principales: | , , , , |
---|---|
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 |
_version_ | 1784604235022729216 |
---|---|
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. |
format | Online Article Text |
id | pubmed-8615971 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2021 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
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 |
work_keys_str_mv | AT huayue manifoldfeaturefusionwithdynamicalfeatureselectionforcrosssubjectemotionrecognition AT zhongxiaolong manifoldfeaturefusionwithdynamicalfeatureselectionforcrosssubjectemotionrecognition AT zhangbingxue manifoldfeaturefusionwithdynamicalfeatureselectionforcrosssubjectemotionrecognition AT yinzhong manifoldfeaturefusionwithdynamicalfeatureselectionforcrosssubjectemotionrecognition AT zhangjianhua manifoldfeaturefusionwithdynamicalfeatureselectionforcrosssubjectemotionrecognition |