Emotion Recognition Using Hierarchical Spatiotemporal Electroencephalogram Information from Local to Global Brain Regions
To understand human emotional states, local activities in various regions of the cerebral cortex and the interactions among different brain regions must be considered. This paper proposes a hierarchical emotional context feature learning model that improves multichannel electroencephalography (EEG)-...
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MDPI AG
2023-09-01
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| Series: | Bioengineering |
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| Online Access: | https://www.mdpi.com/2306-5354/10/9/1040 |
| _version_ | 1827727141659213824 |
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| author | Dong-Ki Jeong Hyoung-Gook Kim Jin-Young Kim |
| author_facet | Dong-Ki Jeong Hyoung-Gook Kim Jin-Young Kim |
| author_sort | Dong-Ki Jeong |
| collection | DOAJ |
| description | To understand human emotional states, local activities in various regions of the cerebral cortex and the interactions among different brain regions must be considered. This paper proposes a hierarchical emotional context feature learning model that improves multichannel electroencephalography (EEG)-based emotion recognition by learning spatiotemporal EEG features from a local brain region to a global brain region. The proposed method comprises a regional brain-level encoding module, a global brain-level encoding module, and a classifier. First, multichannel EEG signals grouped into nine regions based on the functional role of the brain are input into a regional brain-level encoding module to learn local spatiotemporal information. Subsequently, the global brain-level encoding module improved emotional classification performance by integrating local spatiotemporal information from various brain regions to learn the global context features of brain regions related to emotions. Next, we applied a two-layer bidirectional gated recurrent unit (BGRU) with self-attention to the regional brain-level module and a one-layer BGRU with self-attention to the global brain-level module. Experiments were conducted using three datasets to evaluate the EEG-based emotion recognition performance of the proposed method. The results proved that the proposed method achieves superior performance by reflecting the characteristics of multichannel EEG signals better than state-of-the-art methods. |
| first_indexed | 2024-03-10T23:01:18Z |
| format | Article |
| id | doaj.art-4ef68404e953448188c2ed6349aa1da5 |
| institution | Directory Open Access Journal |
| issn | 2306-5354 |
| language | English |
| last_indexed | 2024-03-10T23:01:18Z |
| publishDate | 2023-09-01 |
| publisher | MDPI AG |
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| series | Bioengineering |
| spelling | doaj.art-4ef68404e953448188c2ed6349aa1da52023-11-19T09:36:51ZengMDPI AGBioengineering2306-53542023-09-01109104010.3390/bioengineering10091040Emotion Recognition Using Hierarchical Spatiotemporal Electroencephalogram Information from Local to Global Brain RegionsDong-Ki Jeong0Hyoung-Gook Kim1Jin-Young Kim2Department of Electronic Convergence Engineering, Kwangwoon University, 20 Gwangun-ro, Nowon-gu, Seoul 01897, Republic of KoreaDepartment of Electronic Convergence Engineering, Kwangwoon University, 20 Gwangun-ro, Nowon-gu, Seoul 01897, Republic of KoreaDepartment of ICT Convergence System Engineering, Chonnam National University, 77 Yongbong-ro, Buk-gu, Gwangju 61186, Republic of KoreaTo understand human emotional states, local activities in various regions of the cerebral cortex and the interactions among different brain regions must be considered. This paper proposes a hierarchical emotional context feature learning model that improves multichannel electroencephalography (EEG)-based emotion recognition by learning spatiotemporal EEG features from a local brain region to a global brain region. The proposed method comprises a regional brain-level encoding module, a global brain-level encoding module, and a classifier. First, multichannel EEG signals grouped into nine regions based on the functional role of the brain are input into a regional brain-level encoding module to learn local spatiotemporal information. Subsequently, the global brain-level encoding module improved emotional classification performance by integrating local spatiotemporal information from various brain regions to learn the global context features of brain regions related to emotions. Next, we applied a two-layer bidirectional gated recurrent unit (BGRU) with self-attention to the regional brain-level module and a one-layer BGRU with self-attention to the global brain-level module. Experiments were conducted using three datasets to evaluate the EEG-based emotion recognition performance of the proposed method. The results proved that the proposed method achieves superior performance by reflecting the characteristics of multichannel EEG signals better than state-of-the-art methods.https://www.mdpi.com/2306-5354/10/9/1040emotion recognitionelectroencephalographyhierarchical spatiotemporal featuresself-attentionbidirectional gated recurrent unit |
| spellingShingle | Dong-Ki Jeong Hyoung-Gook Kim Jin-Young Kim Emotion Recognition Using Hierarchical Spatiotemporal Electroencephalogram Information from Local to Global Brain Regions Bioengineering emotion recognition electroencephalography hierarchical spatiotemporal features self-attention bidirectional gated recurrent unit |
| title | Emotion Recognition Using Hierarchical Spatiotemporal Electroencephalogram Information from Local to Global Brain Regions |
| title_full | Emotion Recognition Using Hierarchical Spatiotemporal Electroencephalogram Information from Local to Global Brain Regions |
| title_fullStr | Emotion Recognition Using Hierarchical Spatiotemporal Electroencephalogram Information from Local to Global Brain Regions |
| title_full_unstemmed | Emotion Recognition Using Hierarchical Spatiotemporal Electroencephalogram Information from Local to Global Brain Regions |
| title_short | Emotion Recognition Using Hierarchical Spatiotemporal Electroencephalogram Information from Local to Global Brain Regions |
| title_sort | emotion recognition using hierarchical spatiotemporal electroencephalogram information from local to global brain regions |
| topic | emotion recognition electroencephalography hierarchical spatiotemporal features self-attention bidirectional gated recurrent unit |
| url | https://www.mdpi.com/2306-5354/10/9/1040 |
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