Cargando…
Granger-Causality-Based Multi-Frequency Band EEG Graph Feature Extraction and Fusion for Emotion Recognition
Graph convolutional neural networks (GCN) have attracted much attention in the task of electroencephalogram (EEG) emotion recognition. However, most features of current GCNs do not take full advantage of the causal connection between the EEG signals in different frequency bands during the process of...
Autores principales: | , , , |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2022
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9776073/ https://www.ncbi.nlm.nih.gov/pubmed/36552109 http://dx.doi.org/10.3390/brainsci12121649 |
_version_ | 1784855788612747264 |
---|---|
author | Zhang, Jing Zhang, Xueying Chen, Guijun Zhao, Qing |
author_facet | Zhang, Jing Zhang, Xueying Chen, Guijun Zhao, Qing |
author_sort | Zhang, Jing |
collection | PubMed |
description | Graph convolutional neural networks (GCN) have attracted much attention in the task of electroencephalogram (EEG) emotion recognition. However, most features of current GCNs do not take full advantage of the causal connection between the EEG signals in different frequency bands during the process of constructing the adjacency matrix. Based on the causal connectivity between the EEG channels obtained by Granger causality (GC) analysis, this paper proposes a multi-frequency band EEG graph feature extraction and fusion method for EEG emotion recognition. First, the original GC matrices between the EEG signals at each frequency band are calculated via GC analysis, and then they are adaptively converted to asymmetric binary GC matrices through an optimal threshold. Then, a kind of novel GC-based GCN feature (GC-GCN) is constructed by using differential entropy features and the binary GC matrices as the node values and adjacency matrices, respectively. Finally, on the basis of the GC-GCN features, a new multi-frequency band feature fusion method (GC-F-GCN) is proposed, which integrates the graph information of the EEG signals at different frequency bands for the same node. The experimental results demonstrate that the proposed GC-F-GCN method achieves better recognition performance than the state-of-the-art GCN methods, for which average accuracies of 97.91%, 98.46%, and 98.15% were achieved for the arousal, valence, and arousal–valence classifications, respectively. |
format | Online Article Text |
id | pubmed-9776073 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
spelling | pubmed-97760732022-12-23 Granger-Causality-Based Multi-Frequency Band EEG Graph Feature Extraction and Fusion for Emotion Recognition Zhang, Jing Zhang, Xueying Chen, Guijun Zhao, Qing Brain Sci Article Graph convolutional neural networks (GCN) have attracted much attention in the task of electroencephalogram (EEG) emotion recognition. However, most features of current GCNs do not take full advantage of the causal connection between the EEG signals in different frequency bands during the process of constructing the adjacency matrix. Based on the causal connectivity between the EEG channels obtained by Granger causality (GC) analysis, this paper proposes a multi-frequency band EEG graph feature extraction and fusion method for EEG emotion recognition. First, the original GC matrices between the EEG signals at each frequency band are calculated via GC analysis, and then they are adaptively converted to asymmetric binary GC matrices through an optimal threshold. Then, a kind of novel GC-based GCN feature (GC-GCN) is constructed by using differential entropy features and the binary GC matrices as the node values and adjacency matrices, respectively. Finally, on the basis of the GC-GCN features, a new multi-frequency band feature fusion method (GC-F-GCN) is proposed, which integrates the graph information of the EEG signals at different frequency bands for the same node. The experimental results demonstrate that the proposed GC-F-GCN method achieves better recognition performance than the state-of-the-art GCN methods, for which average accuracies of 97.91%, 98.46%, and 98.15% were achieved for the arousal, valence, and arousal–valence classifications, respectively. MDPI 2022-12-01 /pmc/articles/PMC9776073/ /pubmed/36552109 http://dx.doi.org/10.3390/brainsci12121649 Text en © 2022 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 Zhang, Jing Zhang, Xueying Chen, Guijun Zhao, Qing Granger-Causality-Based Multi-Frequency Band EEG Graph Feature Extraction and Fusion for Emotion Recognition |
title | Granger-Causality-Based Multi-Frequency Band EEG Graph Feature Extraction and Fusion for Emotion Recognition |
title_full | Granger-Causality-Based Multi-Frequency Band EEG Graph Feature Extraction and Fusion for Emotion Recognition |
title_fullStr | Granger-Causality-Based Multi-Frequency Band EEG Graph Feature Extraction and Fusion for Emotion Recognition |
title_full_unstemmed | Granger-Causality-Based Multi-Frequency Band EEG Graph Feature Extraction and Fusion for Emotion Recognition |
title_short | Granger-Causality-Based Multi-Frequency Band EEG Graph Feature Extraction and Fusion for Emotion Recognition |
title_sort | granger-causality-based multi-frequency band eeg graph feature extraction and fusion for emotion recognition |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9776073/ https://www.ncbi.nlm.nih.gov/pubmed/36552109 http://dx.doi.org/10.3390/brainsci12121649 |
work_keys_str_mv | AT zhangjing grangercausalitybasedmultifrequencybandeeggraphfeatureextractionandfusionforemotionrecognition AT zhangxueying grangercausalitybasedmultifrequencybandeeggraphfeatureextractionandfusionforemotionrecognition AT chenguijun grangercausalitybasedmultifrequencybandeeggraphfeatureextractionandfusionforemotionrecognition AT zhaoqing grangercausalitybasedmultifrequencybandeeggraphfeatureextractionandfusionforemotionrecognition |