Cargando…
Electroencephalograph-Based Emotion Recognition Using Brain Connectivity Feature and Domain Adaptive Residual Convolution Model
In electroencephalograph (EEG) emotion recognition research, obtaining high-level emotional features with more discriminative information has become the key to improving the classification performance. This study proposes a new end-to-end emotion recognition method based on brain connectivity (BC) f...
Autores principales: | , , , , , |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Frontiers Media S.A.
2022
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9257260/ https://www.ncbi.nlm.nih.gov/pubmed/35812226 http://dx.doi.org/10.3389/fnins.2022.878146 |
_version_ | 1784741306094845952 |
---|---|
author | Chen, Jingxia Min, Chongdan Wang, Changhao Tang, Zhezhe Liu, Yang Hu, Xiuwen |
author_facet | Chen, Jingxia Min, Chongdan Wang, Changhao Tang, Zhezhe Liu, Yang Hu, Xiuwen |
author_sort | Chen, Jingxia |
collection | PubMed |
description | In electroencephalograph (EEG) emotion recognition research, obtaining high-level emotional features with more discriminative information has become the key to improving the classification performance. This study proposes a new end-to-end emotion recognition method based on brain connectivity (BC) features and domain adaptive residual convolutional network (short for BC-DA-RCNN), which could effectively extract the spatial connectivity information related to the emotional state of the human brain and introduce domain adaptation to achieve accurate emotion recognition within and across the subject’s EEG signals. The BC information is represented by the global brain network connectivity matrix. The DA-RCNN is used to extract high-level emotional features between different dimensions of EEG signals, reduce the domain offset between different subjects, and strengthen the common features between different subjects. The experimental results on the large public DEAP data set show that the accuracy of the subject-dependent and subject-independent binary emotion classification in valence reaches 95.15 and 88.28%, respectively, which outperforms all the benchmark methods. The proposed method is proven to have lower complexity, better generalization ability, and domain robustness that help to lay a solid foundation for the development of high-performance affective brain-computer interface applications. |
format | Online Article Text |
id | pubmed-9257260 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | Frontiers Media S.A. |
record_format | MEDLINE/PubMed |
spelling | pubmed-92572602022-07-07 Electroencephalograph-Based Emotion Recognition Using Brain Connectivity Feature and Domain Adaptive Residual Convolution Model Chen, Jingxia Min, Chongdan Wang, Changhao Tang, Zhezhe Liu, Yang Hu, Xiuwen Front Neurosci Neuroscience In electroencephalograph (EEG) emotion recognition research, obtaining high-level emotional features with more discriminative information has become the key to improving the classification performance. This study proposes a new end-to-end emotion recognition method based on brain connectivity (BC) features and domain adaptive residual convolutional network (short for BC-DA-RCNN), which could effectively extract the spatial connectivity information related to the emotional state of the human brain and introduce domain adaptation to achieve accurate emotion recognition within and across the subject’s EEG signals. The BC information is represented by the global brain network connectivity matrix. The DA-RCNN is used to extract high-level emotional features between different dimensions of EEG signals, reduce the domain offset between different subjects, and strengthen the common features between different subjects. The experimental results on the large public DEAP data set show that the accuracy of the subject-dependent and subject-independent binary emotion classification in valence reaches 95.15 and 88.28%, respectively, which outperforms all the benchmark methods. The proposed method is proven to have lower complexity, better generalization ability, and domain robustness that help to lay a solid foundation for the development of high-performance affective brain-computer interface applications. Frontiers Media S.A. 2022-06-22 /pmc/articles/PMC9257260/ /pubmed/35812226 http://dx.doi.org/10.3389/fnins.2022.878146 Text en Copyright © 2022 Chen, Min, Wang, Tang, Liu and Hu. https://creativecommons.org/licenses/by/4.0/This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms. |
spellingShingle | Neuroscience Chen, Jingxia Min, Chongdan Wang, Changhao Tang, Zhezhe Liu, Yang Hu, Xiuwen Electroencephalograph-Based Emotion Recognition Using Brain Connectivity Feature and Domain Adaptive Residual Convolution Model |
title | Electroencephalograph-Based Emotion Recognition Using Brain Connectivity Feature and Domain Adaptive Residual Convolution Model |
title_full | Electroencephalograph-Based Emotion Recognition Using Brain Connectivity Feature and Domain Adaptive Residual Convolution Model |
title_fullStr | Electroencephalograph-Based Emotion Recognition Using Brain Connectivity Feature and Domain Adaptive Residual Convolution Model |
title_full_unstemmed | Electroencephalograph-Based Emotion Recognition Using Brain Connectivity Feature and Domain Adaptive Residual Convolution Model |
title_short | Electroencephalograph-Based Emotion Recognition Using Brain Connectivity Feature and Domain Adaptive Residual Convolution Model |
title_sort | electroencephalograph-based emotion recognition using brain connectivity feature and domain adaptive residual convolution model |
topic | Neuroscience |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9257260/ https://www.ncbi.nlm.nih.gov/pubmed/35812226 http://dx.doi.org/10.3389/fnins.2022.878146 |
work_keys_str_mv | AT chenjingxia electroencephalographbasedemotionrecognitionusingbrainconnectivityfeatureanddomainadaptiveresidualconvolutionmodel AT minchongdan electroencephalographbasedemotionrecognitionusingbrainconnectivityfeatureanddomainadaptiveresidualconvolutionmodel AT wangchanghao electroencephalographbasedemotionrecognitionusingbrainconnectivityfeatureanddomainadaptiveresidualconvolutionmodel AT tangzhezhe electroencephalographbasedemotionrecognitionusingbrainconnectivityfeatureanddomainadaptiveresidualconvolutionmodel AT liuyang electroencephalographbasedemotionrecognitionusingbrainconnectivityfeatureanddomainadaptiveresidualconvolutionmodel AT huxiuwen electroencephalographbasedemotionrecognitionusingbrainconnectivityfeatureanddomainadaptiveresidualconvolutionmodel |